Title: Learning Independent Features with Adversarial Nets for Non-linear ICA. Abstract: Reliable measures of statistical dependence could potentially be useful tools for learning independent features and performing tasks like source separation using Independent Component Analysis (ICA).  Unfortunately, many of such measures, like the mutual information, are hard to estimate and optimize directly.  We propose to learn independent features with adversarial objectives (Goodfellow et al. 2014, Arjovsky et al. 2017) which optimize such measures implicitly.  These objectives compare samples from the joint distribution and the product of the marginals without the need to compute any probability densities. We also propose two methods for obtaining samples from the product of the marginals using either a simple resampling trick or a separate parametric distribution.  Our experiments show that this strategy can easily be applied to different types of model architectures and solve both linear and non-linear ICA problems.
