  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.
