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.

提出了一种新方法，称为复合因素对齐对称学习（CFASL），该方法整合到 VAEs 中，以实现无监督学习中基于对称性的解缠学习，不需要任何数据集因素信息的先验知识。在定量和深入的定性分析中，CFASL 在单因素变化和多因素变化条件下相对于现有方法显示出了明显的改进。