@conference {ngweta_simple_2023,
	title = {Simple Disentanglement of Style and Content in Visual Representations},
	booktitle = {Proceedings of the 40th International Conference on Machine Learning},
	volume = {202},
	year = {2023},
	note = {ISSN: 2640-3498},
	pages = {26063{\textendash}26086},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {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.},
	url = {https://proceedings.mlr.press/v202/ngweta23a.html},
	author = {Ngweta, Lilian and Maity, Subha and Gittens, Alex and Sun, Yuekai and Yurochkin, Mikhail}
}
