DeepAI AI Chat
Log In Sign Up

AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks

by   Yuesong Tian, et al.
Zhejiang University
Columbia University

Generative Adversarial Networks (GANs) are formulated as minimax game problems, whereby generators attempt to approach real data distributions by virtue of adversarial learning against discriminators. The intrinsic problem complexity poses the challenge to enhance the performance of generative networks. In this work, we aim to boost model learning from the perspective of network architectures, by incorporating recent progress on automated architecture search into GANs. To this end, we propose a fully differentiable search framework for generative adversarial networks, dubbed alphaGAN. The searching process is formalized as solving a bi-level minimax optimization problem, in which the outer-level objective aims for seeking a suitable network architecture towards pure Nash Equilibrium conditioned on the generator and the discriminator network parameters optimized with a traditional GAN loss in the inner level. The entire optimization performs a first-order method by alternately minimizing the two-level objective in a fully differentiable manner, enabling architecture search to be completed in an enormous search space. Extensive experiments on CIFAR-10 and STL-10 datasets show that our algorithm can obtain high-performing architectures only with 3-GPU hours on a single GPU in the search space comprised of approximate 2 ? 1011 possible configurations. We also provide a comprehensive analysis on the behavior of the searching process and the properties of searched architectures, which would benefit further research on architectures for generative models. Pretrained models and codes are available at


page 13

page 14


AdversarialNAS: Adversarial Neural Architecture Search for GANs

Neural Architecture Search (NAS) that aims to automate the procedure of ...

Unchain the Search Space with Hierarchical Differentiable Architecture Search

Differentiable architecture search (DAS) has made great progress in sear...

Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks

Conditional Generative Adversarial Networks (cGAN) were designed to gene...

Coarse-to-Fine Searching for Efficient Generative Adversarial Networks

This paper studies the neural architecture search (NAS) problem for deve...

ChainGAN: A sequential approach to GANs

We propose a new architecture and training methodology for generative ad...

Teachers Do More Than Teach: Compressing Image-to-Image Models

Generative Adversarial Networks (GANs) have achieved huge success in gen...