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Multi-agent Diverse Generative Adversarial Networks
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
doi:10.1109/cvpr.2018.00888
dblp:conf/cvpr/GhoshKNTD18
fatcat:vds2jm3mdrhedoc45phwm7ps6m