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.
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. 