  Learning visual representations with interpretable features, i.e.,
disentangled representations, remains a challenging problem. Existing methods
demonstrate some success but are hard to apply to large-scale vision datasets
like ImageNet. In this work, we propose a simple post-processing framework to
disentangle content and style in learned representations from pre-trained
vision models. We model the pre-trained features probabilistically as linearly
entangled combinations of the latent content and style factors and develop a
simple disentanglement algorithm based on the probabilistic model. We show that
the method provably disentangles content and style features and verify its
efficacy empirically. Our post-processed features yield significant domain
generalization performance improvements when the distribution shift occurs due
to style changes or style-related spurious correlations.
