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Several recent works on self-supervised learning are trained by mappingdifferent augmentations of the same image to the same feature representation.The data augmentations used are of crucial importance to the quality of learnedfeature representations. In this paper, we analyze how the color jittertraditionally used in data augmentation negatively impacts the quality of thecolor features in learned feature representations. To address this problem, wepropose a more realistic, physics-based color data augmentation - which we callPlanckian Jitter - that creates realistic variations in chromaticity andproduces a model robust to illumination changes that can be commonly observedin real life, while maintaining the ability to discriminate image content basedon color information. Experiments confirm that such a representation iscomplementary to the representations learned with the currently-used colorjitter augmentation and that a simple concatenation leads to significantperformance gains on a wide range of downstream datasets. In addition, wepresent a color sensitivity analysis that documents the impact of differenttraining methods on model neurons and shows that the performance of the learnedfeatures is robust with respect to illuminant variations.

Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training