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.

翻译：在变分自编码器（VAE）的解耦学习中，输入向量和潜变量的对称性为此提供了宝贵的见解。然而，目前仅有少数无监督方法被提出，且仍需要训练数据中的已知因子信息。本文提出一种新方法——复合因子对齐对称学习（CFASL），该方法集成到VAE中，可在无监督学习且无数据集因子信息的条件下，实现基于对称性的解耦。CFASL通过三项创新特性来学习基于对称性的解耦：1）注入归纳偏置，通过显式可学习的对称码本，将潜变量维度与因子对齐的对称性对齐；2）通过学习码本中的因子对齐对称性，学习复合对称性以表达两个随机样本间的未知因子变化；3）在上述两种条件下训练VAE时，引入群等变编码器和解码器。此外，本文提出一种针对多因子变化场景的扩展评估指标，用于对比VAE解耦性能。在定量和深入的定性分析中，与现有最先进方法相比，CFASL在单因子变化和多因子变化条件下均展现出显著提升的解耦性能。