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SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping. (arXiv:2209.10623v1 [cs.LG])
Sept. 23, 2022, 1:11 a.m. | Jiageng Zhu, Hanchen Xie, Wael Abd-Almageed
cs.LG updates on arXiv.org arxiv.org
Representation disentanglement is an important goal of representation
learning that benefits various downstream tasks. To achieve this goal, many
unsupervised learning representation disentanglement approaches have been
developed. However, the training process without utilizing any supervision
signal have been proved to be inadequate for disentanglement representation
learning. Therefore, we propose a novel weakly-supervised training approach,
named as SW-VAE, which incorporates pairs of input observations as supervision
signals by using the generative factors of datasets. Furthermore, we introduce
strategies to gradually increase …
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