Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science. In this work, we consider data given as an invertible mixture of two statistically independent components, and assume that one of the components is observed while the other is hidden. Our goal is to recover the hidden component. For this purpose, we propose an autoencoder equipped with a discriminator. Unlike the standard nonlinear ICA problem, which was shown to be non-identifiable, in the special case of ICA we consider here, we show that our approach can recover the component of interest up to entropy-preserving transformation. We demonstrate the performance of the proposed approach on several datasets, including image synthesis, voice cloning, and fetal ECG extraction.

翻译：隐性可变发现是应用科学中广泛应用的数据分析中的一个中心问题。 在这项工作中,我们认为所提供的数据是两个统计独立组成部分的不可置疑的混合体,并假定一个组成部分被观察,而另一个部分被隐藏。我们的目标是回收隐藏的部件。为此目的,我们提议一个装有歧视器的自动编码器。与标准的非线性ICA问题不同,在本文所考虑的ICA的特殊情况下,我们表明我们的方法可以回收利息部分,直到加密-保护变异。我们展示了在包括图像合成、语音克隆和胎儿ECG提取在内的若干数据集上拟议方法的绩效。