We present a self-supervised variational autoencoder (VAE) to jointly learn disentangled and dependent hidden factors and then enhance disentangled representation learning by a self-supervised classifier to eliminate coupled representations in a contrastive manner. To this end, a Contrastive Copula VAE (C$^2$VAE) is introduced without relying on prior knowledge about data in the probabilistic principle and involving strong modeling assumptions on the posterior in the neural architecture. C$^2$VAE simultaneously factorizes the posterior (evidence lower bound, ELBO) with total correlation (TC)-driven decomposition for learning factorized disentangled representations and extracts the dependencies between hidden features by a neural Gaussian copula for copula coupled representations. Then, a self-supervised contrastive classifier differentiates the disentangled representations from the coupled representations, where a contrastive loss regularizes this contrastive classification together with the TC loss for eliminating entangled factors and strengthening disentangled representations. C$^2$VAE demonstrates a strong effect in enhancing disentangled representation learning. C$^2$VAE further contributes to improved optimization addressing the TC-based VAE instability and the trade-off between reconstruction and representation.

翻译：我们提出了一种自监督变分自编码器，用于联合学习解耦与依赖的隐因子，并通过自监督分类器以对比方式消除耦合表示，从而增强解耦表示学习。为此，引入对比连接函数VAE（C$^2$VAE），该方法在概率原理上不依赖关于数据的先验知识，且在神经架构中无需对后验进行强建模假设。C$^2$VAE同时通过总相关驱动的分解对后验（证据下界）进行因子化以学习因子化解耦表示，并利用神经高斯连接函数提取隐特征之间的依赖关系以获取连接耦合表示。随后，一个自监督对比分类器区分解耦表示与耦合表示，其中对比损失与总相关损失共同正则化该对比分类过程，从而消除纠缠因子并强化解耦表示。C$^2$VAE在增强解耦表示学习方面展现出显著效果，并进一步优化了基于总相关的VAE不稳定性及重构与表示之间的权衡问题。