论文标题
Nashae：通过对抗协方差最小化解开表示形式
NashAE: Disentangling Representations through Adversarial Covariance Minimization
论文作者
论文摘要
我们提出了一种自我监督的方法，以解除高维数据变化的因素，该因素不依赖于基本变化概况的先验知识（例如，没有关于要提取的单个潜在变量的数量或分布的假设）。在我们称为nashae的方法中，通过促进从所有其他编码元素中恢复的每个编码元素和恢复的元素的信息之间的差异，在标准自动编码器（AE）的低维潜在空间中完成了高维的特征分离。通过将其作为AE和回归网络合奏之间的Minmax游戏来有效地促进分解，从而构建了这一点，每个游戏都提供了一个以对所有其他元素观察的元素进行估计的估计。我们将我们的方法与使用现有的分离指标进行定量比较我们的方法。此外，我们表明Nashae具有提高的可靠性和提高的能力来捕获学习潜在表示中的显着数据特征。
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.
