  The recently introduced introspective variational autoencoder (IntroVAE)
exhibits outstanding image generations, and allows for amortized inference
using an image encoder. The main idea in IntroVAE is to train a VAE
adversarially, using the VAE encoder to discriminate between generated and real
data samples. However, the original IntroVAE loss function relied on a
particular hinge-loss formulation that is very hard to stabilize in practice,
and its theoretical convergence analysis ignored important terms in the loss.
In this work, we take a step towards better understanding of the IntroVAE
model, its practical implementation, and its applications. We propose the
Soft-IntroVAE, a modified IntroVAE that replaces the hinge-loss terms with a
smooth exponential loss on generated samples. This change significantly
improves training stability, and also enables theoretical analysis of the
complete algorithm. Interestingly, we show that the IntroVAE converges to a
distribution that minimizes a sum of KL distance from the data distribution and
an entropy term. We discuss the implications of this result, and demonstrate
that it induces competitive image generation and reconstruction. Finally, we
describe two applications of Soft-IntroVAE to unsupervised image translation
and out-of-distribution detection, and demonstrate compelling results. Code and
additional information is available on the project website --
https://taldatech.github.io/soft-intro-vae-web
