Metric Learning-based Generative Adversarial Network

TL;DR

This paper introduces MLGAN, a novel GAN training framework that employs dynamic metric learning for improved stability and performance, outperforming existing methods on various datasets.

Contribution

The paper proposes a new GAN training method using a discriminator that learns an adaptive distance metric, enhancing stability and effectiveness.

Findings

MLGAN achieves superior performance over state-of-the-art GANs.

MLGAN increases training stability.

Experimental results on multiple datasets validate the approach.

Abstract

Generative Adversarial Networks (GANs), as a framework for estimating generative models via an adversarial process, have attracted huge attention and have proven to be powerful in a variety of tasks. However, training GANs is well known for being delicate and unstable, partially caused by its sig- moid cross entropy loss function for the discriminator. To overcome such a problem, many researchers directed their attention on various ways to measure how close the model distribution and real distribution are and have applied dif- ferent metrics as their objective functions. In this paper, we propose a novel framework to train GANs based on distance metric learning and we call it Metric Learning-based Gener- ative Adversarial Network (MLGAN). The discriminator of MLGANs can dynamically learn an appropriate metric, rather than a static one, to measure the distance between generated samples and real samples. Afterwards, MLGANs update the generator under the newly learned metric. We evaluate our ap- proach on several representative datasets and the experimen- tal results demonstrate that MLGANs can achieve superior performance compared with several existing state-of-the-art approaches. We also empirically show that MLGANs could increase the stability of training GANs.