Title: Multi-Agent Diverse Generative Adversarial Networks,
Abstract: We propose MAD-GAN, an intuitive generalization to the Generative Adversarial
Networks (GANs) and its conditional variants to address the well known problem
of mode collapse. First, MAD-GAN is a multi-agent GAN architecture
incorporating multiple generators and one discriminator. Second, to enforce
that different generators capture diverse high probability modes, the
discriminator of MAD-GAN is designed such that along with finding the real and
fake samples, it is also required to identify the generator that generated the
given fake sample. Intuitively, to succeed in this task, the discriminator must
learn to push different generators towards different identifiable modes. We
perform extensive experiments on synthetic and real datasets and compare
MAD-GAN with different variants of GAN. We show high quality diverse sample
generations for challenging tasks such as image-to-image translation and face
generation. In addition, we also show that MAD-GAN is able to disentangle
different modalities when trained using highly challenging diverse-class
dataset (e.g. dataset with images of forests, icebergs, and bedrooms). In the
end, we show its efficacy on the unsupervised feature representation task. In
Appendix, we introduce a similarity based competing objective (MAD-GAN-Sim)
which encourages different generators to generate diverse samples based on a
user defined similarity metric. We show its performance on the image-to-image
translation, and also show its effectiveness on the unsupervised feature
representation task.