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- W4301606400 abstract "Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning. We investigate learning group-disentangled representations for groups of factors with weak supervision. Existing techniques to address this challenge merely constrain the approximate posterior by averaging over observations of a shared group. As a result, observations with a common set of variations are encoded to distinct latent representations, reducing their capacity to disentangle and generalize to downstream tasks. In contrast to previous works, we propose GroupVAE, a simple yet effective Kullback-Leibler (KL) divergence-based regularization across shared latent representations to enforce consistent and disentangled representations. We conduct a thorough evaluation and demonstrate that our GroupVAE significantly improves group disentanglement. Further, we demonstrate that learning group-disentangled representations improve upon downstream tasks, including fair classification and 3D shape-related tasks such as reconstruction, classification, and transfer learning, and is competitive to supervised methods." @default.
- W4301606400 created "2022-10-05" @default.
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- W4301606400 date "2021-10-23" @default.
- W4301606400 modified "2023-09-27" @default.
- W4301606400 title "Group-disentangled Representation Learning with Weakly-Supervised Regularization" @default.
- W4301606400 doi "https://doi.org/10.48550/arxiv.2110.12185" @default.
- W4301606400 hasPublicationYear "2021" @default.
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