Visual Transfer Learning: Informal Introduction and Literature Overview
---
paper_title: Recognition-by-components: A theory of human image understanding

paper_content:

The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N £ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensiona l image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position an$ image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or is degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. Representational power derives from an allowance of free combinations of the geons. A Principle of Componential Recovery can account for the major phenomena of object recognition: If an arrangement of two or three geons can be recovered from the input, objects can be quickly recognized even when they are occluded, novel, rotated in depth, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory. Any single object can project an infinity of image configurations to the retina. The orientation of the object to the viewer can vary continuously, each giving rise to a different two-dimensional projection. The object can be occluded by other objects or texture fields, as when viewed behind foliage. The object need not be presented as a full-colored textured image but instead can be a simplified line drawing. Moreover, the object can even be missing some of its parts or be a novel exemplar of its particular category. But it is only with rare exceptions that an image fails to be rapidly and readily classified, either as an instance of a familiar object category or as an instance that cannot be so classified (itself a form of classification).
---
paper_title: LabelMe: Online Image Annotation and Applications

paper_content:

Central to the development of computer vision systems is the collection and use of annotated images spanning our visual world. Annotations may include information about the identity, spatial extent, and viewpoint of the objects present in a depicted scene. Such a database is useful for the training and evaluation of computer vision systems. Motivated by the availability of images on the Internet, we introduced a web-based annotation tool that allows online users to label objects and their spatial extent in images. To date, we have collected over 400 000 annotations that span a variety of different scene and object classes. In this paper, we show the contents of the database, its growth over time, and statistics of its usage. In addition, we explore and survey applications of the database in the areas of computer vision and computer graphics. Particularly, we show how to extract the real-world 3-D coordinates of images in a variety of scenes using only the user-provided object annotations. The output 3-D information is comparable to the quality produced by a laser range scanner. We also characterize the space of the images in the database by analyzing 1) statistics of the co-occurrence of large objects in the images and 2) the spatial layout of the labeled images.
---
paper_title: Comparative object similarity for improved recognition with few or no examples

paper_content:

Learning models for recognizing objects with few or no training examples is important, due to the intrinsic long-tailed distribution of objects in the real world. In this paper, we propose an approach to use comparative object similarity. The key insight is that: given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. We develop a regularized kernel machine algorithm to use this category dependent similarity regularization. Our experiments on hundreds of categories show that our method can make significant improvement, especially for categories with no examples.
---
paper_title: A discriminatively trained, multiscale, deformable part model

paper_content:

This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.
---
paper_title: The role of features, algorithms and data in visual recognition

paper_content:

There are many computer vision algorithms developed for visual (scene and object) recognition. Some systems focus on involved learning algorithms, some leverage millions of training images, and some systems focus on modeling relevant information (features) with the goal of effective recognition. However, none of these systems come close to human capabilities. If we study human responses on similar problems we could gain insight into which of the three factors (1) learning algorithm (2) amount of training data and (3) features is critical to humans' superior performance. In this work we take a small step towards this goal by performing a series of human studies and machine experiments. We find no evidence that human pattern matching algorithms are better than standard machine learning algorithms. Moreover, we find that humans don't leverage increased amounts of training data. Through statistical analysis on the machine experiments and supporting human studies, we find that the main factor impacting accuracies is the choice of features.
---
paper_title: The Pascal Visual Object Classes (VOC) Challenge

paper_content:

The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
---
paper_title: ImageNet: A large-scale hierarchical image database

paper_content:

The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.
---
paper_title: Automated Visual Inspection: A Survey

paper_content:

This paper surveys publications, reports, and articles dealing with automated visual inspection for industry. The references are organized according to their contents: overview and discussions, rationales, components and design considerations, commercially available systems, applications. A number of applications and their inspection methodologies are discussed in detail: the inspection of printed circuit boards, photomasks, integrated circuit chips. Other inspection applications are listed as a bibliography. A list of selectively annotated references in commercially available visual inspection tools is also included.
---
paper_title: Face Recognition from a Single Image per Person : A Survey

paper_content:

One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower cost for storing and processing them. Unfortunately, many reported face recognition techniques rely heavily on the size and representative of training set, and most of them will suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. This situation is called ''one sample per person'' problem: given a stored database of faces, the goal is to identify a person from the database later in time in any different and unpredictable poses, lighting, etc. from just one image. Such a task is very challenging for most current algorithms due to the extremely limited representative of training sample. Numerous techniques have been developed to attack this problem, and the purpose of this paper is to categorize and evaluate these algorithms. The prominent algorithms are described and critically analyzed. Relevant issues such as data collection, the influence of the small sample size, and system evaluation are discussed, and several promising directions for future research are also proposed in this paper.
---
paper_title: Deep Self-Taught Learning for Handwritten Character Recognition

paper_content:

Recent theoretical and empirical work in statistical machine learning has demonstrated the importance of learning algorithms for deep architectures, i.e., function classes obtained by composing multiple non-linear transformations. Self-taught learning (exploiting unlabeled examples or examples from other distributions) has already been applied to deep learners, but mostly to show the advantage of unlabeled examples. Here we explore the advantage brought by out-of-distribution examples. For this purpose we developed a powerful generator of stochastic variations and noise processes for character images, including not only affine transformations but also slant, local elastic deformations, changes in thickness, background images, grey level changes, contrast, occlusion, and various types of noise. The out-of-distribution examples are obtained from these highly distorted images or by including examples of object classes different from those in the target test set. We show that deep learners benefit more from out-of-distribution examples than a corresponding shallow learner, at least in the area of handwritten character recognition. In fact, we show that they beat previously published results and reach human-level performance on both handwritten digit classification and 62-class handwritten character recognition.
---
paper_title: Caltech-256 Object Category Dataset

paper_content:

We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Caltech-256 is collected in a similar manner with several improvements: a) the number of categories is more than doubled, b) the minimum number of images in any category is increased from 31 to 80, c) artifacts due to image rotation are avoided and d) a new and larger clutter category is introduced for testing background rejection. We suggest several testing paradigms to measure classification performance, then benchmark the dataset using two simple metrics as well as a state-of-the-art spatial pyramid matching [2] algorithm. Finally we use the clutter category to train an interest detector which rejects uninformative background regions.
---
paper_title: On the mathematical foundations of learning

paper_content:

(1) A main theme of this report is the relationship of approximation to learning and the primary role of sampling (inductive inference). We try to emphasize relations of the theory of learning to the mainstream of mathematics. In particular, there are large roles for probability theory, for algorithms such as least squares, and for tools and ideas from linear algebra and linear analysis. An advantage of doing this is that communication is facilitated and the power of core mathematics is more easily brought to bear. We illustrate what we mean by learning theory by giving some instances. (a) The understanding of language acquisition by children or the emergence of languages in early human cultures. (b) In Manufacturing Engineering, the design of a new wave of machines is anticipated which uses sensors to sample properties of objects before, during, and after treatment. The information gathered from these samples is to be analyzed by the machine to decide how to better deal with new input objects (see [43]). (c) Pattern recognition of objects ranging from handwritten letters of the alphabet to pictures of animals, to the human voice. Understanding the laws of learning plays a large role in disciplines such as (Cognitive) Psychology, Animal Behavior, Economic Decision Making, all branches of Engineering, Computer Science, and especially the study of human thought processes (how the brain works). Mathematics has already played a big role towards the goal of giving a universal foundation of studies in these disciplines. We mention as examples the theory of Neural Networks going back to McCulloch and Pitts [25] and Minsky and Papert [27], the PAC learning of Valiant [40], Statistical Learning Theory as developed by Vapnik [42], and the use of reproducing kernels as in [17] among many other mathematical developments. We are heavily indebted to these developments. Recent discussions with a number of mathematicians have also been helpful. In
---
paper_title: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

paper_content:

From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
---
paper_title: Semi-supervised Learning in Gigantic Image Collections

paper_content:

With the advent of the Internet it is now possible to collect hundreds of millions of images. These images come with varying degrees of label information. "Clean labels" can be manually obtained on a small fraction, "noisy labels" may be extracted automatically from surrounding text, while for most images there are no labels at all. Semi-supervised learning is a principled framework for combining these different label sources. However, it scales polynomially with the number of images, making it impractical for use on gigantic collections with hundreds of millions of images and thousands of classes. In this paper we show how to utilize recent results in machine learning to obtain highly efficient approximations for semi-supervised learning that are linear in the number of images. Specifically, we use the convergence of the eigenvectors of the normalized graph Laplacian to eigenfunctions of weighted Laplace-Beltrami operators. Our algorithm enables us to apply semi-supervised learning to a database of 80 million images gathered from the Internet.
---
paper_title: The Nature of Statistical Learning Theory

paper_content:

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
---
paper_title: Bayesian Inference and Optimal Design for the Sparse Linear Model

paper_content:

The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori sparse solutions and neglect to represent posterior uncertainties. In this paper, we address problems of Bayesian optimal design (or experiment planning), for which accurate estimates of uncertainty are essential. To this end, we employ expectation propagation approximate inference for the linear model with Laplace prior, giving new insight into numerical stability properties and proposing a robust algorithm. We also show how to estimate model hyperparameters by empirical Bayesian maximisation of the marginal likelihood, and propose ideas in order to scale up the method to very large underdetermined problems. ::: ::: We demonstrate the versatility of our framework on the application of gene regulatory network identification from micro-array expression data, where both the Laplace prior and the active experimental design approach are shown to result in significant improvements. We also address the problem of sparse coding of natural images, and show how our framework can be used for compressive sensing tasks. ::: ::: Part of this work appeared in Seeger et al. (2007b). The gene network identification application appears in Steinke et al. (2007).
---
paper_title: Learning a meta-level prior for feature relevance from multiple related tasks

paper_content:

In many prediction tasks, selecting relevant features is essential for achieving good generalization performance. Most feature selection algorithms consider all features to be a priori equally likely to be relevant. In this paper, we use transfer learning---learning on an ensemble of related tasks---to construct an informative prior on feature relevance. We assume that features themselves have meta-features that are predictive of their relevance to the prediction task, and model their relevance as a function of the meta-features using hyperparameters (called meta-priors). We present a convex optimization algorithm for simultaneously learning the meta-priors and feature weights from an ensemble of related prediction tasks which share a similar relevance structure. Our approach transfers the "meta-priors" among different tasks, which makes it possible to deal with settings where tasks have nonoverlapping features or the relevance of the features vary over the tasks. We show that learning feature relevance improves performance on two real data sets which illustrate such settings: (1) predicting ratings in a collaborative filtering task, and (2) distinguishing arguments of a verb in a sentence.
---
paper_title: Learning to Learn

paper_content:

Preface. Part I: Overview Articles. 1. Learning to Learn: Introduction and Overview S. Thrun, L. Pratt. 2. A Survey of Connectionist Network Reuse Through Transfer L. Pratt, B. Jennings. 3. Transfer in Cognition A. Robins. Part II: Prediction. 4. Theoretical Models of Learning to Learn J. Baxter. 5. Multitask Learning R. Caruana. 6. Making a Low-Dimensional Representation Suitable for Diverse Tasks N. Intrator, S. Edelman. 7. The Canonical Distortion Measure for Vector Quantization and Function Approximation J. Baxter. 8. Lifelong Learning Algorithms S. Thrun. Part III: Relatedness. 9. The Parallel Transfer of Task Knowledge Using Dynamic Learning Rates Based on a Measure of Relatedness D.L. Silver, R.E. Mercer. 10. Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge S. Thrun, J. O'Sullivan. Part IV: Control. 11. CHILD: A First Step Towards Continual Learning M.B. Ring. 12. Reinforcement Learning with Self-Modifying Policies J. Schmidhuber, et al. 13. Creating Advice-Taking Reinforcement Learners R. Maclin, J.W. Shavlik. Contributing Authors. Index.
---
paper_title: Language Transfer: Cross-Linguistic Influence in Language Learning

paper_content:

Series editors' preface Preface Introduction 2. Earlier thinking on transfer 3. Some fundamental problems in the study of transfer 4. Discourse 5. Semantics 6. Syntax 7. Phonetics, phonology, and writing systems 8. Nonstructural factors in transfer 9. Looking back and looking ahead 10. Implications for teaching Glossary References Language index Author index Subject index.
---
paper_title: Metalearning: Applications to Data Mining

paper_content:

Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.
---
paper_title: Learning to Learn

paper_content:

Preface. Part I: Overview Articles. 1. Learning to Learn: Introduction and Overview S. Thrun, L. Pratt. 2. A Survey of Connectionist Network Reuse Through Transfer L. Pratt, B. Jennings. 3. Transfer in Cognition A. Robins. Part II: Prediction. 4. Theoretical Models of Learning to Learn J. Baxter. 5. Multitask Learning R. Caruana. 6. Making a Low-Dimensional Representation Suitable for Diverse Tasks N. Intrator, S. Edelman. 7. The Canonical Distortion Measure for Vector Quantization and Function Approximation J. Baxter. 8. Lifelong Learning Algorithms S. Thrun. Part III: Relatedness. 9. The Parallel Transfer of Task Knowledge Using Dynamic Learning Rates Based on a Measure of Relatedness D.L. Silver, R.E. Mercer. 10. Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge S. Thrun, J. O'Sullivan. Part IV: Control. 11. CHILD: A First Step Towards Continual Learning M.B. Ring. 12. Reinforcement Learning with Self-Modifying Policies J. Schmidhuber, et al. 13. Creating Advice-Taking Reinforcement Learners R. Maclin, J.W. Shavlik. Contributing Authors. Index.
---
paper_title: Transfering Nonlinear Representations using Gaussian Processes with a Shared Latent Space

paper_content:

When a series of problems are related, representations derived from learning earlier tasks may be useful in solving later problems. In this paper we propose a novel approach to transfer learning with low-dimensional, non-linear latent spaces. We show how such representations can be jointly learned across multiple tasks in a Gaussian Process framework. When transferred to new tasks with relatively few training examples, learning can be faster and/or more accurate. Experiments on digit recognition and newsgroup classification tasks show significantly improved performance when compared to baseline performance with a representation derived from a semi-supervised learning approach or with a discriminative approach that uses only the target data.
---
paper_title: Is Learning The n-th Thing Any Easier Than Learning The First?

paper_content:

This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks.
---
paper_title: Face recognition from one example view

paper_content:

To create a pose-invariant face recognizer, one strategy is the view-based approach, which uses a set of real example views at different poses. But what if we only have one real view available, such as a scanned passport photo-can we still recognize faces under different poses? Given one real view at a known pose, it is still possible to use the view-based approach by exploiting prior knowledge of faces to generate virtual views, or views of the face as seen from different poses. To represent prior knowledge, we use 2D example views of prototype faces under different rotations. We develop example-based techniques for applying the rotation seen in the prototypes to essentially "rotate" the single real view which is available. Next, the combined set of one real and multiple virtual views is used as example views for a view-based, pose-invariant face recognizer. Oar experiments suggest that among the techniques for expressing prior knowledge of faces, 2D example-based approaches should be considered alongside the more standard 3D modeling techniques. >
---
paper_title: Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

paper_content:

Summarising a high dimensional data set with a low dimensional embedding is a standard approach for exploring its structure. In this paper we provide an overview of some existing techniques for discovering such embeddings. We then introduce a novel probabilistic interpretation of principal component analysis (PCA) that we term dual probabilistic PCA (DPPCA). The DPPCA model has the additional advantage that the linear mappings from the embedded space can easily be non-linearised through Gaussian processes. We refer to this model as a Gaussian process latent variable model (GP-LVM). Through analysis of the GP-LVM objective function, we relate the model to popular spectral techniques such as kernel PCA and multidimensional scaling. We then review a practical algorithm for GP-LVMs in the context of large data sets and develop it to also handle discrete valued data and missing attributes. We demonstrate the model on a range of real-world and artificially generated data sets.
---
paper_title: Kernel Sharing With Joint Boosting For Multi-Class Concept Detection

paper_content:

Object/scene detection by discriminative kernel-based classification has gained great interest due to its promising performance and flexibility. In this paper, unlike traditional approaches that independently build binary classifiers to detect individual concepts, we proposed a new framework for multi-class concept detection based on kernel sharing and joint learning. By sharing "good" kernels among concepts, accuracy of individual weak detectors can be greatly improved; by joint learning of common detectors among classes, the required kernels and the computational complexity for detecting each individual concept can be reduced. We demonstrated our approach by developing an extended JointBoost framework, which was used to choose the optimal kernel and subset of sharing classes in an iterative boosting process. In addition, we constructed multi-resolution visual vocabularies by hierarchical clustering and computed kernels based on spatial matching. We tested our method in detecting 12 concepts (objects, scenes, etc) over 80+ hours of broadcast news videos from the challenging TRECVID 2005 corpus. Significant performance gains were achieved -10% in mean average precision (MAP) and up to 34% average precision (AP) for some concepts like maps, building, and boat-ship. Extensive analysis of the results also revealed interesting and important underlying relations among concepts.
---
paper_title: Online and batch learning of pseudo-metrics

paper_content:

We describe and analyze an online algorithm for supervised learning of pseudo-metrics. The algorithm receives pairs of instances and predicts their similarity according to a pseudo-metric. The pseudo-metrics we use are quadratic forms parameterized by positive semi-definite matrices. The core of the algorithm is an update rule that is based on successive projections onto the positive semi-definite cone and onto half-space constraints imposed by the examples. We describe an efficient procedure for performing these projections, derive a worst case mistake bound on the similarity predictions, and discuss a dual version of the algorithm in which it is simple to incorporate kernel operators. The online algorithm also serves as a building block for deriving a large-margin batch algorithm. We demonstrate the merits of the proposed approach by conducting experiments on MNIST dataset and on document filtering.
---
paper_title: Learning to share visual appearance for multiclass object detection

paper_content:

We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether.
---
paper_title: Learning Visual Representations using Images with Captions

paper_content:

Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples is small. When labeled data is scarce it may be beneficial to use unlabeled data to learn an image representation that is low-dimensional, but nevertheless captures the information required to discriminate between image categories. This paper describes a method for learning representations from large quantities of unlabeled images which have associated captions; the goal is to improve learning in future image classification problems. Experiments show that our method significantly outperforms (1) a fully-supervised baseline model, (2) a model that ignores the captions and learns a visual representation by performing PCA on the unlabeled images alone and (3) a model that uses the output of word classifiers trained using captions and unlabeled data. Our current work concentrates on captions as the source of meta-data, but more generally other types of meta-data could be used.
---
paper_title: Learning from One Example through Shared Densities on Transforms

paper_content:

We define a process called congealing in which elements of a dataset (images) are brought into correspondence with each other jointly, producing a data-defined model. It is based upon minimizing the summed component-wise (pixel-wise) entropies over a continuous set of transforms on the data. One of the biproducts of this minimization is a set of transform, one associated with each original training sample. We then demonstrate a procedure for effectively bringing test data into correspondence with the data-defined model produced in the congealing process. Subsequently; we develop a probability density over the set of transforms that arose from the congealing process. We suggest that this density over transforms may be shared by many classes, and demonstrate how using this density as "prior knowledge" can be used to develop a classifier based on only a single training example for each class.
---
paper_title: Online domain adaptation of a pre-trained cascade of classifiers

paper_content:

Many classifiers are trained with massive training sets only to be applied at test time on data from a different distribution. How can we rapidly and simply adapt a classifier to a new test distribution, even when we do not have access to the original training data? We present an on-line approach for rapidly adapting a “black box” classifier to a new test data set without retraining the classifier or examining the original optimization criterion. Assuming the original classifier outputs a continuous number for which a threshold gives the class, we reclassify points near the original boundary using a Gaussian process regression scheme. We show how this general procedure can be used in the context of a classifier cascade, demonstrating performance that far exceeds state-of-the-art results in face detection on a standard data set. We also draw connections to work in semi-supervised learning, domain adaptation, and information regularization.
---
paper_title: Sharing Visual Features for Multiclass and Multiview Object Detection

paper_content:

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection
---
paper_title: Learning to Locate Informative Features for Visual Identification

paper_content:

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object's exact identity (e.g. Bob's BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive "training" examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient "same" versus "different" classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.
---
paper_title: Localization vs. identification of semi-algebraic sets

paper_content:

How difficult is it to find the position of a known object using random samples? We study this question, which is central to Computer Vision and Robotics, in a formal way. We compare the information complexity of two types of tasks: the task of identificationof an unknown object from labeled examples input, and the task of localization in which the identity of the target is known and its location in some background scene has to be determined. We carry out the comparison of these tasks using two measuring rods for the complexity of classes of sets; The Vapnik-Chervonenkis dimension and the†-entropy of relevant classes. The VC-dimension analysis yields bounds on the sample complexity of performing these tasks in the PAC-learning scenario whereas the†-entropy parameter reflects the complexity of the relevant learning tasks when the examples are generated by the uniform distribution (over the background scene). Our analysis provides a mathematical ground to the intuition that localization is indeed much easier than identification. Our upper-bounds on the hardness of localization are established by applying a new, algebraic-geometry based, general tool for the calculation of the VC-dimension of classes of algebraically defined objects. This technique was independently discovered by Goldberg and Jerrum. We believe that our techniques will prove useful for further VC-dimension estimation problems.
---
paper_title: Online multiclass learning by interclass hypothesis sharing

paper_content:

We describe a general framework for online multiclass learning based on the notion of hypothesis sharing. In our framework sets of classes are associated with hypotheses. Thus, all classes within a given set share the same hypothesis. This framework includes as special cases commonly used constructions for multiclass categorization such as allocating a unique hypothesis for each class and allocating a single common hypothesis for all classes. We generalize the multiclass Perceptron to our framework and derive a unifying mistake bound analysis. Our construction naturally extends to settings where the number of classes is not known in advance but, rather, is revealed along the online learning process. We demonstrate the merits of our approach by comparing it to previous methods on both synthetic and natural datasets.
---
paper_title: Learning Multiple Tasks with Kernel Methods

paper_content:

We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task learning. Our analysis shows that the problem of estimating many task functions with regularization can be cast as a single task learning problem if a family of multi-task kernel functions we define is used. These kernels model relations among the tasks and are derived from a novel form of regularizers. Specific kernels that can be used for multi-task learning are provided and experimentally tested on two real data sets. In agreement with past empirical work on multi-task learning, the experiments show that learning multiple related tasks simultaneously using the proposed approach can significantly outperform standard single-task learning particularly when there are many related tasks but few data per task.
---
paper_title: Uncovering shared structures in multiclass classification

paper_content:

This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.
---
paper_title: Object class recognition by unsupervised scale-invariant learning

paper_content:

We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and relative scale. An entropy-based feature detector is used to select regions and their scale within the image. In learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images. The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).
---
paper_title: A shape-based object class model for knowledge transfer

paper_content:

Object class models trained on hundreds or thousands of images have shown to enable robust detection. Transferring knowledge from such models to new object classes trained from a few or even as little as one training instance however is still in its infancy. This paper designs a shape-based model that allows to easily and explicitly transfer knowledge on three different levels: transfer of individual parts' shape and appearance information, transfer of local symmetry between parts, and transfer of part topology. Due to the factorized form of the model, knowledge can either be transferred for the complete model or just partial knowledge corresponding to certain aspects of the model. The experiments clearly demonstrate that the proposed model is competitive with the state-of-the-art and enables both full and partial knowledge transfer.
---
paper_title: Learning Multiple Tasks with Kernel Methods

paper_content:

We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task learning. Our analysis shows that the problem of estimating many task functions with regularization can be cast as a single task learning problem if a family of multi-task kernel functions we define is used. These kernels model relations among the tasks and are derived from a novel form of regularizers. Specific kernels that can be used for multi-task learning are provided and experimentally tested on two real data sets. In agreement with past empirical work on multi-task learning, the experiments show that learning multiple related tasks simultaneously using the proposed approach can significantly outperform standard single-task learning particularly when there are many related tasks but few data per task.
---
paper_title: Semiparametric latent factor models

paper_content:

We propose a semiparametric model for regression and classification problems involving multiple response variables. The model makes use of a set of Gaussian processes to model the relationship to the inputs in a nonparametric fashion. Conditional dependencies between the responses can be captured through a linear mixture of the driving processes. This feature becomes important if some of the responses of predictive interest are less densely supplied by observed data than related auxiliary ones. We propose an efficient approximate inference scheme for this semiparametric model whose complexity is linear in the number of training data points.
---
paper_title: Gaussian Processes for Object Categorization

paper_content:

Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gaussian Processes (GPs) provide a framework for deriving regression techniques with explicit uncertainty models; we show here how Gaussian Processes with covariance functions defined based on a Pyramid Match Kernel (PMK) can be used for probabilistic object category recognition. Our probabilistic formulation provides a principled way to learn hyperparameters, which we utilize to learn an optimal combination of multiple covariance functions. It also offers confidence estimates at test points, and naturally allows for an active learning paradigm in which points are optimally selected for interactive labeling. We show that with an appropriate combination of kernels a significant boost in classification performance is possible. Further, our experiments indicate the utility of active learning with probabilistic predictive models, especially when the amount of training data labels that may be sought for a category is ultimately very small.
---
paper_title: Kernel multi-task learning using task-specific features

paper_content:

In this paper we are concerned with multitask learning when task-specific features are available. We describe two ways of achieving this using Gaussian process predictors: in the first method, the data from all tasks is combined into one dataset, making use of the task-specific features. In the second method we train specific predictors for each reference task, and then combine their predictions using a gating network. We demonstrate these methods on a compiler performance prediction problem, where a task is defined as predicting the speed-up obtained when applying a sequence of code transformations to a given program.
---
paper_title: Learning Gaussian process kernels via hierarchical Bayes

paper_content:

We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystrom method, which results in a complex, data driven kernel. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance.
---
paper_title: Extensions of the Informative Vector Machine

paper_content:

The informative vector machine (IVM) is a practical method for Gaussian process regression and classification. The IVM produces a sparse approximation to a Gaussian process by combining assumed density filtering with a heuristic for choosing points based on minimizing posterior entropy. This paper extends IVM in several ways. First, we propose a novel noise model that allows the IVM to be applied to a mixture of labeled and unlabeled data. Second, we use IVM on a block-diagonal covariance matrix, for learning to learn from related tasks. Third, we modify the IVM to incorporate prior knowledge from known invariances. All of these extensions are tested on artificial and real data.
---
paper_title: Uncovering shared structures in multiclass classification

paper_content:

This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.
---
paper_title: Gaussian Process Models for Link Analysis and Transfer Learning

paper_content:

This paper aims to model relational data on edges of networks. We describe appropriate Gaussian Processes (GPs) for directed, undirected, and bipartite networks. The inter-dependencies of edges can be effectively modeled by adapting the GP hyper-parameters. The framework suggests an intimate connection between link prediction and transfer learning, which were traditionally two separate research topics. We develop an efficient learning algorithm that can handle a large number of observations. The experimental results on several real-world data sets verify superior learning capacity.
---
paper_title: Multi-task Gaussian Process Prediction

paper_content:

In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a shared covariance function on input-dependent features and a "free-form" covariance matrix over tasks. This allows for good flexibility when modelling inter-task dependencies while avoiding the need for large amounts of data for training. We show that under the assumption of noise-free observations and a block design, predictions for a given task only depend on its target values and therefore a cancellation of inter-task transfer occurs. We evaluate the benefits of our model on two practical applications: a compiler performance prediction problem and an exam score prediction task. Additionally, we make use of GP approximations and properties of our model in order to provide scalability to large data sets.
---
paper_title: Geometric context from a single image

paper_content:

Many computer vision algorithms limit their performance by ignoring the underlying 3D geometric structure in the image. We show that we can estimate the coarse geometric properties of a scene by learning appearance-based models of geometric classes, even in cluttered natural scenes. Geometric classes describe the 3D orientation of an image region with respect to the camera. We provide a multiple-hypothesis framework for robustly estimating scene structure from a single image and obtaining confidences for each geometric label. These confidences can then be used to improve the performance of many other applications. We provide a thorough quantitative evaluation of our algorithm on a set of outdoor images and demonstrate its usefulness in two applications: object detection and automatic single-view reconstruction.
---
paper_title: Objects in Context

paper_content:

In the task of visual object categorization, semantic context can play the very important role of reducing ambiguity in objects' visual appearance. In this work we propose to incorporate semantic object context as a post-processing step into any off-the-shelf object categorization model. Using a conditional random field (CRF) framework, our approach maximizes object label agreement according to contextual relevance. We compare two sources of context: one learned from training data and another queried from Google Sets. The overall performance of the proposed framework is evaluated on the PASCAL and MSRC datasets. Our findings conclude that incorporating context into object categorization greatly improves categorization accuracy.
---
paper_title: Robust real-time object detection

paper_content:

This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [4]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performance comparable to the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. Author email: fPaul.Viola,Mike.J.Jonesg@compaq.com c Compaq Computer Corporation, 2001 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of the Cambridge Research Laboratory of Compaq Computer Corporation in Cambridge, Massachusetts; an acknowledgment of the authors and individual contributors to the work; and all applicable portions of the copyright notice. Copying, reproducing, or republishing for any other purpose shall require a license with payment of fee to the Cambridge Research Laboratory. All rights reserved. CRL Technical reports are available on the CRL’s web page at http://crl.research.compaq.com. Compaq Computer Corporation Cambridge Research Laboratory One Cambridge Center Cambridge, Massachusetts 02142 USA
---
paper_title: Zero-data learning of new tasks

paper_content:

We introduce the problem of zero-data learning, where a model must generalize to classes or tasks for which no training data are available and only a description of the classes or tasks are provided. Zero-data learning is useful for problems where the set of classes to distinguish or tasks to solve is very large and is not entirely covered by the training data. The main contributions of this work lie in the presentation of a general formalization of zero-data learning, in an experimental analysis of its properties and in empirical evidence showing that generalization is possible and significant in this context. The experimental work of this paper addresses two classification problems of character recognition and a multitask ranking problem in the context of drug discovery. Finally, we conclude by discussing how this new framework could lead to a novel perspective on how to extend machine learning towards AI, where an agent can be given a specification for a learning problem before attempting to solve it (with very few or even zero examples).
---
paper_title: Attribute-centric recognition for cross-category generalization

paper_content:

We propose an approach to find and describe objects within broad domains. We introduce a new dataset that provides annotation for sharing models of appearance and correlation across categories. We use it to learn part and category detectors. These serve as the visual basis for an integrated model of objects. We describe objects by the spatial arrangement of their attributes and the interactions between them. Using this model, our system can find animals and vehicles that it has not seen and infer attributes, such as function and pose. Our experiments demonstrate that we can more reliably locate and describe both familiar and unfamiliar objects, compared to a baseline that relies purely on basic category detectors.
---
paper_title: WordNet::Similarity - Measuring The Relatedness Of Concepts

paper_content:

WordNet: Similarity is a freely available software package that makes it possible to measure the semantic similarity or relatedness between a pair of concepts (or word senses). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return a numeric value that represents the degree to which they are similar or related.
---
paper_title: Zero-Shot Learning with Semantic Output Codes

paper_content:

We consider the problem of zero-shot learning, where the goal is to learn a classifier f : X → Y that must predict novel values of Y that were omitted from the training set. To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes. We provide a formalism for this type of classifier and study its theoretical properties in a PAC framework, showing conditions under which the classifier can accurately predict novel classes. As a case study, we build a SOC classifier for a neural decoding task and show that it can often predict words that people are thinking about from functional magnetic resonance images (fMRI) of their neural activity, even without training examples for those words.
---
paper_title: Comparative object similarity for improved recognition with few or no examples

paper_content:

Learning models for recognizing objects with few or no training examples is important, due to the intrinsic long-tailed distribution of objects in the real world. In this paper, we propose an approach to use comparative object similarity. The key insight is that: given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. We develop a regularized kernel machine algorithm to use this category dependent similarity regularization. Our experiments on hundreds of categories show that our method can make significant improvement, especially for categories with no examples.
---
paper_title: Describing objects by their attributes

paper_content:

We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.
---
paper_title: Mutual boosting for contextual inference

paper_content:

Mutual Boosting is a method aimed at incorporating contextual information to augment object detection. When multiple detectors of objects and parts are trained in parallel using AdaBoost [1], object detectors might use the remaining intermediate detectors to enrich the weak learner set. This method generalizes the efficient features suggested by Viola and Jones [2] thus enabling information inference between parts and objects in a compositional hierarchy. In our experiments eye-, nose-, mouth- and face detectors are trained using the Mutual Boosting framework. Results show that the method outperforms applications overlooking contextual information. We suggest that achieving contextual integration is a step toward human-like detection capabilities.
---
paper_title: Evaluating knowledge transfer and zero-shot learning in a large-scale setting

paper_content:

While knowledge transfer (KT) between object classes has been accepted as a promising route towards scalable recognition, most experimental KT studies are surprisingly limited in the number of object classes considered. To support claims of KT w.r.t. scalability we thus advocate to evaluate KT in a large-scale setting. To this end, we provide an extensive evaluation of three popular approaches to KT on a recently proposed large-scale data set, the ImageNet Large Scale Visual Recognition Competition 2010 data set. In a first setting they are directly compared to one-vs-all classification often neglected in KT papers and in a second setting we evaluate their ability to enable zero-shot learning. While none of the KT methods can improve over one-vs-all classification they prove valuable for zero-shot learning, especially hierarchical and direct similarity based KT. We also propose and describe several extensions of the evaluated approaches that are necessary for this large-scale study.
---
paper_title: The Nature of Statistical Learning Theory

paper_content:

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
---
paper_title: Learning to detect unseen object classes by between-class attribute transfer

paper_content:

We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new large-scale dataset, “Animals with Attributes”, of over 30,000 animal images that match the 50 classes in Osherson's classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes.
---
paper_title: The more you know , the less you learn : from knowledge transfer to one-shot learning of object categories

paper_content:

Learning a category from few examples is a challenging task for vision algorithms, while psychological studies have shown that humans are able to generalise correctly even from a single instance (one-shot learning). The most accredited hypothesis is that humans are able to exploit prior knowledge when learning a new related category. This paper presents an SVM-based model adaptation algorithm able to perform knowledge transfer for a new category when very limited examples are available. Using a leave- one-out estimate of the weighted error-rate the algorithm automatically decides from where to transfer (on which known category to rely), how much to transfer (the degree of adaptation) and if it is worth transferring something at all. Moreover a weighted least-squares loss function takes optimally care of data unbalance between negative and positive examples. Experiments presented on two different object category databases show that the proposed method is able to exploit previous knowledge avoiding negative transfer. The overall classification performance is increased compared to what would be achieved by starting from scratch. Furthermore as the number of already learned categories grows, the algorithm is able to learn a new category from one sample with increasing precision, i.e. it is able to perform one-shot learning.
---
paper_title: Discovering Structure in Multiple Learning Tasks: The TC Algorithm

paper_content:

Recently, there has been an increased interest in “lifelong” machine learning methods, that transfer knowledge across multiple learning tasks. Such methods have repeatedly been found to outperform conventional, single-task learning algorithms when the learning tasks are appropriately related. To increase robustness of such approaches, methods are desirable that can reason about the relatedness of individual learning tasks, in order to avoid the danger arising from tasks that are unrelated and thus potentially misleading. This paper describes the task-clustering (TC) algorithm. TC clusters learning tasks into classes of mutually related tasks. When facing a new learning task, TC first determines the most related task cluster, then exploits information selectively from this task cluster only. An empirical study carried out in a mobile robot domain shows that TC outperforms its non-selective counterpart in situations where only a small number of tasks is relevant.
---
paper_title: Efficient Case Based Feature Construction for Heterogeneous Learning Tasks

paper_content:

Feature construction is essential for solving many complex learning problems. Unfortunately, the construction of features usually implies searching a very large space of possibilities and is often computationally demanding. In this work, we propose a case based approach to feature construction. Learning tasks are stored together with a corresponding set of constructed features in a case base and can be retrieved to speed up feature construction for new tasks. The essential part of our method is a new representation model for learning tasks and a corresponding distance measure. Learning tasks are compared using relevance weights on a common set of base features only. Therefore, the case base can be built and queried very efficiently. In this respect, our approach is unique and enables us to apply case based feature construction not only on a large scale, but also in distributed learning scenarios in which communication costs play an important role. We derive a distance measure for heterogenious learning tasks by stating a set of necessary conditions. Although the conditions are quite basic, they constraint the set of applicable methods to a surprisingly small number. We also provide some experimental evidence for the utility of our method. ∗This work was supported by the Deutsche Forschungsgemeinschaft (DFG) as a part of the Collaborative Research Center ”Computational Intelligence” (SFB 531)
---
paper_title: Safety in numbers: Learning categories from few examples with multi model knowledge transfer

paper_content:

Learning object categories from small samples is a challenging problem, where machine learning tools can in general provide very few guarantees. Exploiting prior knowledge may be useful to reproduce the human capability of recognizing objects even from only one single view. This paper presents an SVM-based model adaptation algorithm able to select and weight appropriately prior knowledge coming from different categories. The method relies on the solution of a convex optimization problem which ensures to have the minimal leave-one-out error on the training set. Experiments on a subset of the Caltech-256 database show that the proposed method produces better results than both choosing one single prior model, and transferring from all previous experience in a flat uninformative way.
---
paper_title: An algorithm for transfer learning in a heterogeneous environment

paper_content:

We consider the problem of learning in an environment of classification tasks. Tasks sampled from the environment are used to improve classification performance on future tasks. We consider situations in which the tasks can be divided into groups. Tasks within each group are related by sharing a low dimensional representation, which differs across the groups. We present an algorithm which divides the sampled tasks into groups and computes a common representation for each group. We report experiments on a synthetic and two image data sets, which show the advantage of the approach over single-task learning and a previous transfer learning method.
---

Title: Visual Transfer Learning: Informal Introduction and Literature Overview

Section 1: Motivation
Description 1: This section discusses the human ability to recognize a large variety of object categories with few examples, compares it to the limitations of current machine vision systems, and introduces the role of transfer learning in improving machine vision efficiency.

Section 2: Industrial Applications
Description 2: This section outlines various industrial applications where a lack of learning examples is a significant problem, including face identification, writer or speaker verification, speech or handwritten text recognition, user preference prediction, and automatic visual inspection.

Section 3: Challenges
Description 3: This section details the challenges and problems traditional machine learning methods face in scenarios with few training examples, such as high variability in data and the curse of dimensionality.

Section 4: The Importance of Prior Knowledge
Description 4: This section explains the role of prior knowledge in machine learning and how it is essential to solve ill-posed optimization problems inherent in learning with few examples.

Section 5: Knowledge Transfer and Transfer Learning
Description 5: This section introduces the concept of transfer learning, distinguishing it from traditional machine learning methods, and explains the process of transferring knowledge from previously learned tasks to new tasks.

Section 6: Literature Overview: Transfer Learning
Description 6: This section provides a comprehensive overview of the literature on transfer learning, focusing on methods and principles related to the problem of learning with few examples.

Section 7: What and how to transfer?
Description 7: This section reviews various types of knowledge that can be transferred between tasks, including learning transformations, shared kernels, shared features, and shared latent spaces.

Section 8: Constellation Model and Hierarchical Bayesian Learning
Description 8: This section discusses approaches for knowledge transfer between visual object categories using generative constellation models and hierarchical Bayesian learning.

Section 9: Joint Regularization
Description 9: This section covers methods that include joint regularization terms shared between tasks, such as multitask learning with SVM and sparse solutions using L1,2 regularization.

Section 10: Shared Prior on Latent Functions
Description 10: This section explores the framework of Gaussian processes for modeling a shared prior distribution of latent functions in a multitask setting.

Section 11: Semantic Attributes and Similarities
Description 11: This section discusses the use of semantic attributes and similarities for transfer learning, especially in scenarios with very few or no examples of the target task (zero-shot learning).

Section 12: Context Information
Description 12: This section explains how dissimilar categories can provide useful contextual information for object detection and semantic segmentation tasks.

Section 13: Heterogeneous Transfer: From Where to Transfer?
Description 13: This section addresses the challenge of automatically selecting appropriate support tasks from a large set and presents methods for task clustering and model selection.