Dynamically Grown Generative Adversarial Networks

TL;DR

This paper introduces a method for dynamically growing GANs during training, combining architecture search with gradient-based optimization to automate design choices and achieve state-of-the-art image generation results.

Contribution

It presents a novel automated approach to progressively grow GAN architectures during training, reducing manual design and enhancing performance.

Findings

Achieved new state-of-the-art image generation quality.

Automated architecture search improves GAN training stability.

Insights into generator-discriminator balance and layer choices.

Abstract

Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under-explored and needs manual design for different image data. In this paper, we propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator. It enjoys the benefits of both eased training because of progressive growing and improved performance because of broader architecture design space. Experimental results demonstrate new state-of-the-art of image generation. Observations in the search procedure also provide constructive insights into the GAN model design such as generator-discriminator balance and convolutional layer choices.