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Learning visual representations with interpretable features, i.e.,disentangled representations, remains a challenging problem. Existing methodsdemonstrate some success but are hard to apply to large-scale vision datasetslike ImageNet. In this work, we propose a simple post-processing framework todisentangle content and style in learned representations from pre-trainedvision models. We model the pre-trained features probabilistically as linearlyentangled combinations of the latent content and style factors and develop asimple disentanglement algorithm based on the probabilistic model. We show thatthe method provably disentangles content and style features and verify itsefficacy empirically. Our post-processed features yield significant domaingeneralization performance improvements when the distribution shift occurs dueto style changes or style-related spurious correlations.

Simple Disentanglement of Style and Content in Visual Representations