We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e.g., no assumptions on the number or distribution of the individual latent variables to be extracted). In this method which we call NashAE, high-dimensional feature disentanglement is accomplished in the low-dimensional latent space of a standard autoencoder (AE) by promoting the discrepancy between each encoding element and information of the element recovered from all other encoding elements. Disentanglement is promoted efficiently by framing this as a minmax game between the AE and an ensemble of regression networks which each provide an estimate of an element conditioned on an observation of all other elements. We quantitatively compare our approach with leading disentanglement methods using existing disentanglement metrics. Furthermore, we show that NashAE has increased reliability and increased capacity to capture salient data characteristics in the learned latent representation.

本文提出了一种自监督的方法，用于在不需要先验知识的情况下对高维数据的变异因素进行解缠，即不假设提取的个体潜在变量的数量或分布，通过在标准自动编码器（AE）的低维潜在空间中促进每个编码元素与其他所有编码元素恢复的元素信息之间的差异来实现高维特征解缠，通过将其视为AE和回归网络集合之间的极小极大博弈实现高效的解缠。我们通过使用现有的解缠度量来定量比较我们的方法与领先的解缠方法，此外，我们还展示了NashAE具有更高的可靠性和更高的捕捉学习潜在表示中显着数据特征的能力。

NashAE: 对抗协方差最小化解开表示的混乱