Generative Multi-Adversarial Networks
This work addresses training instability in GANs for image generation, offering an incremental improvement over standard methods.
The paper tackles the challenge of training generative adversarial networks (GANs) by proposing Generative Multi-Adversarial Networks (GMAN), which extends GANs to multiple discriminators, enabling reliable training with the original objective and producing higher quality image samples in fewer iterations, as measured by a pairwise GAM-type metric.
Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the \emph{Generative Multi-Adversarial Network} (GMAN), a framework that extends GANs to multiple discriminators. In previous work, the successful training of GANs requires modifying the minimax objective to accelerate training early on. In contrast, GMAN can be reliably trained with the original, untampered objective. We explore a number of design perspectives with the discriminator role ranging from formidable adversary to forgiving teacher. Image generation tasks comparing the proposed framework to standard GANs demonstrate GMAN produces higher quality samples in a fraction of the iterations when measured by a pairwise GAM-type metric.