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# AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE

*Proceedings of The 14th Asian Conference on Machine Learning*, PMLR 189:658-673, 2023.

#### Abstract

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.