  It has been conjectured that the Fisher divergence is more robust to model
uncertainty than the conventional Kullback-Leibler (KL) divergence. This
motivates the design of a new class of robust generative auto-encoders (AE)
referred to as Fisher auto-encoders. Our approach is to design Fisher AEs by
minimizing the Fisher divergence between the intractable joint distribution of
observed data and latent variables, with that of the postulated/modeled joint
distribution. In contrast to KL-based variational AEs (VAEs), the Fisher AE can
exactly quantify the distance between the true and the model-based posterior
distributions. Qualitative and quantitative results are provided on both MNIST
and celebA datasets demonstrating the competitive performance of Fisher AEs in
terms of robustness compared to other AEs such as VAEs and Wasserstein AEs.
