Multi-agent Diverse Generative Adversarial Networks

Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H.S. Torr, Puneet K. Dokania
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
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
more » ... enerator 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.
doi:10.1109/cvpr.2018.00888 dblp:conf/cvpr/GhoshKNTD18 fatcat:vds2jm3mdrhedoc45phwm7ps6m