Unbalanced GANs: Pre-training the Generator of Generative Adversarial Network using Variational Autoencoder
This addresses training instability in GANs for researchers and practitioners, but it is incremental as it builds on existing VAE and GAN methods.
The paper tackles the problem of unstable training and mode collapse in GANs by pre-training the generator with a VAE, resulting in reduced mode collapses and improved image quality, faster convergence, and stabilized learning at early epochs.
We propose Unbalanced GANs, which pre-trains the generator of the generative adversarial network (GAN) using variational autoencoder (VAE). We guarantee the stable training of the generator by preventing the faster convergence of the discriminator at early epochs. Furthermore, we balance between the generator and the discriminator at early epochs and thus maintain the stabilized training of GANs. We apply Unbalanced GANs to well known public datasets and find that Unbalanced GANs reduce mode collapses. We also show that Unbalanced GANs outperform ordinary GANs in terms of stabilized learning, faster convergence and better image quality at early epochs.