Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues. We present Gaussian Channel Autoencoder (GCAE), a method which achieves reliable disentanglement via flexible density estimation of the latent space. GCAE avoids the curse of dimensionality of density estimation by disentangling subsets of its latent space with the Dual Total Correlation (DTC) metric, thereby representing its high-dimensional latent joint distribution as a collection of many low-dimensional conditional distributions. In our experiments, GCAE achieves highly competitive and reliable disentanglement scores compared with state-of-the-art baselines.

翻译：解耦学习表示在众多应用中展现出巨大潜力，但目前仍面临严重的可靠性问题。我们提出高斯信道自编码器（GCAE），该方法通过对潜在空间进行灵活密度估计实现了可靠的解耦表示。GCAE采用双总相关（DTC）度量对潜在空间子集进行解耦，将高维潜在联合分布表示为多个低维条件分布的集合，从而避免了密度估计的维度灾难。实验结果表明，与现有最先进基线方法相比，GCAE在解耦评分上实现了极具竞争力且可靠的性能。