Variational auto-encoders (VAEs) provide an attractive solution to image generation problem. However, they tend to produce blurred and over-smoothed images due to their dependence on pixel-wise reconstruction loss. This paper introduces a new approach to alleviate this problem in the VAE based generative models. Our model simultaneously learns to match the data, reconstruction loss and the latent distributions of real and fake images to improve the quality of generated samples. To compute the loss distributions, we introduce an auto-encoder based discriminator model which allows an adversarial learning procedure. The discriminator in our model also provides perceptual guidance to the VAE by matching the learned similarity metric of the real and fake samples in the latent space. To stabilize the overall training process, our model uses an error feedback approach to maintain the equilibrium between competing networks in the model. Our experiments show that the generated samples from our proposed model exhibit a diverse set of attributes and facial expressions and scale up to high-resolution images very well.
Our work is a continuation of effort to devise stable deep generative models which produce diverse and improved quality images with high resolution. To this end, different from previous works, our proposed model learns to simultaneously match the data distributions, loss distributions and latent distributions of real and fake data during the training stage. We show that such a hierarchical but flexible supervision in the model helps generate images with better quality and high resolution. The closest to our approach is the BEGAN @cite_32 with notable differences including the maximization of a lower bound (Sec. ), perceptual guidance in the latent space (Sec. ), the combination of data and loss distribution matching in the training objective (Sec. ) and incorporation of VAEs in the generator module to learn a latent space where an image of desired style can deterministically be generated using vector arithmetics (Sec. ).
@cite_32
2605195953
We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks. This method balances the generator and discriminator during training. Additionally, it provides a new approximate convergence measure, fast and stable training and high visual quality. We also derive a way of controlling the trade-off between image diversity and visual quality. We focus on the image generation task, setting a new milestone in visual quality, even at higher resolutions. This is achieved while using a relatively simple model architecture and a standard training procedure.