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Lawn Crystal – Printed Textile Faisalfabrics pk Ittehad Suit Cl-3501b 3 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.
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Comments: This is an updated version of our CVPR'18 paper with the same title. In this version, we also introduce MAD-GAN-Sim in Appendix B
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:1704.02906 [cs.CV]
  (or arXiv:1704.02906v3 [cs.CV] for this version)
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From: Viveka Kulharia [ view email]
[v1] Mon, 10 Apr 2017 15:26:23 UTC (8,524 KB)
[v2] Fri, 9 Mar 2018 23:29:16 UTC (8,208 KB)
[v3] Mon, 16 Jul 2018 16:21:52 UTC (8,452 KB)