Web: http://arxiv.org/abs/2209.07007

Sept. 16, 2022, 1:15 a.m. | Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama

cs.CV updates on arXiv.org arxiv.org

Learning concise data representations without supervisory signals is a
fundamental challenge in machine learning. A prominent approach to this goal is
likelihood-based models such as variational autoencoders (VAE) to learn latent
representations based on a meta-prior, which is a general premise assumed
beneficial for downstream tasks (e.g., disentanglement). However, such
approaches often deviate from the original likelihood architecture to apply the
introduced meta-prior, causing undesirable changes in their training. In this
paper, we propose a novel representation learning method, Gromov-Wasserstein …


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