Adversarial Code Learning for Image Generation

Abstract

We introduce the "adversarial code learning" (ACL) module that improves overall image generation performance to several types of deep models. Instead of performing a posterior distribution modeling in the pixel spaces of generators, ACLs aim to jointly learn a latent code with another image encoder/inference net, with a prior noise as its input. We conduct the learning in an adversarial learning process, which bears a close resemblance to the original GAN but again shifts the learning from image spaces to prior and latent code spaces. ACL is a portable module that brings up much more flexibility and possibilities in generative model designs. First, it allows flexibility to convert non-generative models like Autoencoders and standard classification models to decent generative models. Second, it enhances existing GANs' performance by generating meaningful codes and images from any part of the prior. We have incorporated our ACL module with the aforementioned frameworks and have performed experiments on synthetic, MNIST, CIFAR-10, and CelebA datasets. Our models have achieved significant improvements which demonstrated the generality for image generation tasks.

References in corpus (19)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• Energy-based Generative Adversarial Network
• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• Adversarially Learned Inference
• Autoencoding beyond pixels using a learned similarity metric
• Invertible Conditional GANs for image editing
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
• Neural Photo Editing with Introspective Adversarial Networks
• Learning What and Where to Draw
• Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
• McGan: Mean and Covariance Feature Matching GAN
• Hierarchical Adversarially Learned Inference
• Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
• Semi-Supervised Learning via New Deep Network Inversion