<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ngweta, Lilian</style></author><author><style face="normal" font="default" size="100%">Maity, Subha</style></author><author><style face="normal" font="default" size="100%">Gittens, Alex</style></author><author><style face="normal" font="default" size="100%">Sun, Yuekai</style></author><author><style face="normal" font="default" size="100%">Yurochkin, Mikhail</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simple Disentanglement of Style and Content in Visual Representations</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 40th International Conference on Machine Learning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://proceedings.mlr.press/v202/ngweta23a.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">PMLR</style></publisher><volume><style face="normal" font="default" size="100%">202</style></volume><pages><style face="normal" font="default" size="100%">26063–26086</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><notes><style face="normal" font="default" size="100%">ISSN: 2640-3498</style></notes></record></records></xml>