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：分解的学习表现在许多应用中大有用处,但目前它们存在严重的可靠性问题。我们介绍了高山频道自动编码器(GCAE),这种方法通过对潜在空间进行灵活的密度估计,实现了可靠的分解。 GCAE避免了密度估计的维度的诅咒,因为它的潜伏空间子集与二元完全关联(DTC)指标脱钩,从而代表了其高维潜伏联合分布,作为许多低维有条件分布的集合。 在我们的实验中,GCAE取得了与最新基线的高度竞争性和可靠的分解分数。