Metric Learning-based Generative Adversarial Network
This addresses the problem of unstable GAN training for researchers and practitioners in machine learning, though it appears incremental as it builds on existing metric-based improvements.
The paper tackles the instability in training Generative Adversarial Networks (GANs) by proposing a Metric Learning-based GAN (MLGAN) that dynamically learns an appropriate metric for the discriminator, achieving superior performance on several datasets compared to state-of-the-art approaches.
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.