Papers › Where and What? Examining Interpretable Disentangled Representations

Where and What? Examining Interpretable Disentangled Representations

7 Apr 2021CVPR 2021 1arXiv:2104.05622archive 2025-07-28

Xinqi Zhu, Chang Xu, DaCheng Tao

Capturing interpretable variations has long been one of the goals in disentanglement learning. However, unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the unsupervised setting. In this paper, we examine the interpretability of disentangled representations by investigating two questions: where to be interpreted and what to be interpreted? A latent code is easily to be interpreted if it would consistently impact a certain subarea of the resulting generated image. We thus propose to learn a spatial mask to localize the effect of each individual latent dimension. On the other hand, interpretability usually comes from latent dimensions that capture simple and basic variations in data. We thus impose a perturbation on a certain dimension of the latent code, and expect to identify the perturbation along this dimension from the generated images so that the encoding of simple variations can be enforced. Additionally, we develop an unsupervised model selection method, which accumulates perceptual distance scores along axes in the latent space. On various datasets, our models can learn high-quality disentangled representations without supervision, showing the proposed modeling of interpretability is an effective proxy for achieving unsupervised disentanglement.

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convert_to_shape zhuxinqimac/PS-SC/dataset_tool.py official repository unverified Apache-2.0 (permissive) · da94c36a397beb5b · report
create_rot_mat zhuxinqimac/PS-SC/plot_latent_space.py official repository unverified Apache-2.0 (permissive) · ac86c7ae82552d8c · report
execute_cmdline zhuxinqimac/PS-SC/dataset_tool.py official repository unverified Apache-2.0 (permissive) · dea74cd7a714496f · report
expand_traversal zhuxinqimac/PS-SC/run_generator_ps_sc.py official repository unverified Apache-2.0 (permissive) · 693b845ab6cda741 · report
get_path_or_url zhuxinqimac/PS-SC/pretrained_networks.py official repository unverified Apache-2.0 (permissive) · 28ef046211377170 · report
sample_grid_z zhuxinqimac/PS-SC/plot_latent_space.py official repository unverified Apache-2.0 (permissive) · caf3c1796c3d5058 · report
str_to_intlist zhuxinqimac/PS-SC/dataset_tool.py official repository unverified Apache-2.0 (permissive) · c9e27a8d2f508edc · report
truncated_z_sample zhuxinqimac/PS-SC/run_generate_attr_dataset.py official repository unverified Apache-2.0 (permissive) · b72f0c209b7b64be · report

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