Your Image is My Video: Reshaping the Receptive Field via Image-To-Video
  Differentiable AutoAugmentation and Fusion
Sofia Casarin Cynthia I. Ugwu Sergio Escalera Oswald Lanz
The landscape of deep learning research is moving towards innovative
strategies to harness the true potential of data. Traditionally, emphasis has
been on scaling model architectures, resulting in large and complex neural
networks, which can be difficult to train with limited computational resources.
However, independently of the model size, data quality (i.e. amount and
variability) is still a major factor that affects model generalization. In this
work, we propose a novel technique to exploit available data through the use of
automatic data augmentation for the tasks of image classification and semantic
segmentation. We introduce the first Differentiable Augmentation Search method
(DAS) to generate variations of images that can be processed as videos.
Compared to previous approaches, DAS is extremely fast and flexible, allowing
the search on very large search spaces in less than a GPU day. Our intuition is
that the increased receptive field in the temporal dimension provided by DAS
could lead to benefits also to the spatial receptive field. More specifically,
we leverage DAS to guide the reshaping of the spatial receptive field by
selecting task-dependant transformations. As a result, compared to standard
augmentation alternatives, we improve in terms of accuracy on ImageNet,
Cifar10, Cifar100, Tiny-ImageNet, Pascal-VOC-2012 and CityScapes datasets when
plugging-in our DAS over different light-weight video backbones.