CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder

• URL: http://arxiv.org/abs/2401.08897v2
• Date: Fri, 19 Jan 2024 02:39:59 GMT
• Title: CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder
• Authors: Hee-Jun Jung, Jaehyoung Jeong and Kangil Kim
• Abstract summary: We propose a novel method, Composite Factor-Aligned Symmetry Learning (CFASL), which is integrated into VAEs for learning symmetry-based disentanglement. CFASL incorporates three novel features for learning symmetry-based disentanglement. CFASL demonstrates a significant improvement of disentanglement in single-factor change, and multi-factor change conditions.
• Score: 2.3020018305241337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Symmetries of input and latent vectors have provided valuable insights for disentanglement learning in VAEs.However, only a few works were proposed as an unsupervised method, and even these works require known factor information in training data. We propose a novel method, Composite Factor-Aligned Symmetry Learning (CFASL), which is integrated into VAEs for learning symmetry-based disentanglement in unsupervised learning without any knowledge of the dataset factor information.CFASL incorporates three novel features for learning symmetry-based disentanglement: 1) Injecting inductive bias to align latent vector dimensions to factor-aligned symmetries within an explicit learnable symmetry codebook 2) Learning a composite symmetry to express unknown factors change between two random samples by learning factor-aligned symmetries within the codebook 3) Inducing group equivariant encoder and decoder in training VAEs with the two conditions. In addition, we propose an extended evaluation metric for multi-factor changes in comparison to disentanglement evaluation in VAEs. In quantitative and in-depth qualitative analysis, CFASL demonstrates a significant improvement of disentanglement in single-factor change, and multi-factor change conditions compared to state-of-the-art methods.

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