  Reliable measures of statistical dependence could 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 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.
