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：输入与潜在向量的对称性为变分自编码器（VAE）中的解耦学习提供了重要见解。然而，仅有少数工作提出无监督方法，且这些方法仍需训练数据中的已知因子信息。我们提出了一种新方法——复合因子对齐对称性学习（CFASL），该方法集成于VAE中，可在完全无监督且无需数据集中任何因子信息的前提下，实现基于对称性的解耦学习。CFASL融合了三个创新特性以学习基于对称性的解耦：1) 注入归纳偏置，在显式可学习的对称性码本中将潜在向量维度对齐至因子对齐的对称性；2) 通过学习码本内的因子对齐对称性，构建复合对称性以表达两个随机样本间的未知因子变化；3) 在满足上述两个条件的VAE训练中，引入群等变编码器和解码器。此外，针对多因子变化场景，我们提出了一种扩展评估指标，以对比VAE中的解耦评估效果。在定量分析与深度定性分析中，CFASL在单因子变化及多因子变化条件下，相较于现有最先进方法均展现出显著更优的解耦性能。