Reviews: Adversarial Symmetric Variational Autoencoder

The paper proposes a variant of the Variational Auto-Encoder training objective. It uses adversarial training, to minimize a symmetric KL divergence between the joint distributions of latent and observed variables p(z,x) p(z)p_\theta(x z) and q(z,x) q(x)q_\phi(z x) . The approach is similar to the recent [ Mescheder, Nowozin, Geiger. Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks, 2016 ] in its joining VAE and GAN-like objective, but it is original in that it minimizes a symmetric KL divergence (with a GAN-like objective), which appears crucial to achieve good quality samples. It is also reminiscent of ALI [ Dumoulin et al.