TY - JOUR
T1 - Multi-adversarial autoencoders
T2 - Stable, faster and self-adaptive representation learning
AU - Wu, Xinyu
AU - Jang, Hyeryung
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2025/3/1
Y1 - 2025/3/1
N2 - The variational autoencoder (VAE) and generative adversarial networks (GAN) are two prominent approaches to achieving a probabilistic generative model by way of an autoencoder and a two-player minimax game. While VAEs often suffer from over-simplified posterior approximations, the adversarial autoencoder (AAE) has shown promise by adopting GAN to match the variational posterior to an arbitrary prior through adversarial training. Both VAEs and GANs face significant challenges such as training stability, mode collapse, and difficulty in extracting meaningful latent representations. In this paper, we propose the Multi-adversarial Autoencoder (MAAE), which extends the AAE framework by incorporating multiple discriminators and enabling soft-ensemble feedback. By adaptively regulating the collective feedback from multiple discriminators, MAAE captures a balance between fitting the data distribution and performing accurate inference and accelerates training stability while extracting meaningful and interpretable latent representations. Experimental evaluations on MNIST, CIFAR10, and CelebA datasets demonstrate significant improvements in latent representation, quality of generated samples, log-likelihood, and a pairwise comparison metric, with comparisons to recent methods.
AB - The variational autoencoder (VAE) and generative adversarial networks (GAN) are two prominent approaches to achieving a probabilistic generative model by way of an autoencoder and a two-player minimax game. While VAEs often suffer from over-simplified posterior approximations, the adversarial autoencoder (AAE) has shown promise by adopting GAN to match the variational posterior to an arbitrary prior through adversarial training. Both VAEs and GANs face significant challenges such as training stability, mode collapse, and difficulty in extracting meaningful latent representations. In this paper, we propose the Multi-adversarial Autoencoder (MAAE), which extends the AAE framework by incorporating multiple discriminators and enabling soft-ensemble feedback. By adaptively regulating the collective feedback from multiple discriminators, MAAE captures a balance between fitting the data distribution and performing accurate inference and accelerates training stability while extracting meaningful and interpretable latent representations. Experimental evaluations on MNIST, CIFAR10, and CelebA datasets demonstrate significant improvements in latent representation, quality of generated samples, log-likelihood, and a pairwise comparison metric, with comparisons to recent methods.
KW - Generative models
KW - Multiple discriminators
KW - Mutual information
KW - Representation learning
KW - Variational inference
UR - http://www.scopus.com/inward/record.url?scp=85207251676&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2024.125554
DO - 10.1016/j.eswa.2024.125554
M3 - Article
AN - SCOPUS:85207251676
SN - 0957-4174
VL - 262
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 125554
ER -