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 MADGAN 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