  Recently, introspective models like IntroVAE and S-IntroVAE have excelled in
image generation and reconstruction tasks. The principal characteristic of
introspective models is the adversarial learning of VAE, where the encoder
attempts to distinguish between the real and the fake (i.e., synthesized)
images. However, due to the unavailability of an effective metric to evaluate
the difference between the real and the fake images, the posterior collapse and
the vanishing gradient problem still exist, reducing the fidelity of the
synthesized images. In this paper, we propose a new variation of IntroVAE
called Adversarial Similarity Distance Introspective Variational Autoencoder
(AS-IntroVAE). We theoretically analyze the vanishing gradient problem and
construct a new Adversarial Similarity Distance (AS-Distance) using the
2-Wasserstein distance and the kernel trick. With weight annealing on
AS-Distance and KL-Divergence, the AS-IntroVAE are able to generate stable and
high-quality images. The posterior collapse problem is addressed by making
per-batch attempts to transform the image so that it better fits the prior
distribution in the latent space. Compared with the per-image approach, this
strategy fosters more diverse distributions in the latent space, allowing our
model to produce images of great diversity. Comprehensive experiments on
benchmark datasets demonstrate the effectiveness of AS-IntroVAE on image
generation and reconstruction tasks.
