通过对抗性协方差最小化来解纠缠表征

Eric C. Yeats, Frank Liu, David A. P. Womble, Hai Li
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引用次数: 4

摘要

我们提出了一种自监督方法来解开高维数据中的变化因素,该方法不依赖于对潜在变化概况的先验知识(例如,不假设要提取的单个潜在变量的数量或分布)。该方法在标准自编码器(AE)的低维潜在空间中,通过提高每个编码元素与从所有其他编码元素中恢复的元素信息之间的差异来实现高维特征解纠缠。通过将其构建为AE和回归网络集合之间的最小最大博弈,有效地促进了解纠缠,每个回归网络都提供了对所有其他元素的观察为条件的元素的估计。我们定量地比较了我们的方法与领先的解纠缠方法使用现有的解纠缠度量。此外,我们表明NashAE在学习潜在表征中具有更高的可靠性和捕获显著数据特征的能力。
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NashAE: Disentangling Representations through Adversarial Covariance Minimization
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.
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