AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE

Understand

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).