This is an empirical paper which constructs color invariant networks and evaluates their performances on a realistic data set. The paper studies the simplest possible case of color invariance: invariance under pixel-wise permutation of the color channels. Thus the network is aware not of the specific color object, but its colorfulness. The data set introduced in the paper consists of images showing crashed cars from which ten classes were extracted. An additional annotation was done which labeled whether the car shown was red or non-red.   The networks were evaluated by their performance on the classification task. With the color annotation we altered the color ratios  in the training data and analyzed the generalization capabilities of the networks on the unaltered test data. We further split the test data in red and non-red cars and did a similar evaluation. It is shown in the paper that an pixel-wise ordering of the rgb-values of the images performs better or at least similarly for small deviations from the true color ratios. The limits of these networks are also discussed. Imagine a training set without red objects, and a test set which contains red objects. How well does a trained net perform? This is not a mere academic question. Imagine we want to separate cars, humans and free space for an autonomous driving task. If our data contained red cars but not red trousers say, it will most likely classify legs as cars. Even worse it could mix up yellow markings and yellow trouser and classify an human as free space. On the other hand we can not disregard color all together as it yields some clues for natural objects such as trees, sky, mist, snow and also some man made objects such as markings, or traffic signs. The first thing that comes to mind is to balance the color statistics of our data set. But this impossible to do in practice, and worse at training time it is unknown which colors will become fashion in say five years. What is called for is network which is invariant under color changes. In this paper we construct and analyze such a network. We compared different color invariant neural networks. It is shown in the paper that only pixel-wise ordering of the color channels shows similar results on cifar10 (and also on the crashed car data set). To test the hypothesis that ordering is invariant under color changes, a classification task has been extracted from a publicly available crashed car data set. In addition each car was labeled as red or non-red. On this data set it was shown that all three nets showed similar behavior on all cars and on all non-red cars, on the red cars the order nets performed noticeably better. Further, we excluded red cars from the training set, and showed that the weighted order nets performed better than the baseline on all three test sets. On the red cars the order showed significantly better results. Further, we fixed the ratio of red / non-red in the training sets. The order nets perform better or at least similar to the baseline net. All nets degrade noticeable while increasing the ratio red cars. As a teaser we report in the appendix there all three nets fail. No net can cope with one class of entirely red cars and all other classes set to non red.We can also view the paper as an empirical study on generalization: trained nets are tested on a statistically different test set. Most plots of accuracy over iterations on the test set showed overshooting despite of the l 2 regularization in the final layer. The curve of the weighted net in FIG2 being a typical example. We interpret this as over fitting, training should be stopped much earlier. A further empirical conclusion shows that sub-sampling the unevenly distributed test data gave similar results than deriving the accuracies for all class separately and then taking the mean. But the individual class may perform rather poor, an insight which is lost in sub-sampling.The paper introduced and evaluated a variant of color invariant nets. The constructed nets are invariant under pixel-wise permutation of the color channels. Thus the network is aware not of the specific color, but the colorfulness of the object. Further, a data set was introduced which allowed to evaluate color invariance in a realistic setting. We see that the net constructed in the paper are better or equal to the baseline if the color distribution is not to far away from the true distribution. We conclude that colorfulness is enough information for classification. The crash car data set itself calls for further experiments and insights, and remains a tough classification challenge.
We construct and evaluate color invariant neural nets on a novel realistic data set Proposes a method to make neural networks for image recognition color invariant and evaluates it on the cifar 10 dataset. The authors investigate a modified input layer that results in color invariant networks, and show that certain color invariant input layers can improve accuracy for test-images from a different color distribution than the training images. The authors test a CNN on images with color channels modified to be invariant to permutations, with performance not degraded by too much. 