Abstract. We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G ...
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We propose a new framework for estimating generative models via an adversar- ial process, in which we simultaneously train two models: a generative model G.
Missing: q= 3A% 2Fpaper% 2F5423-
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Jun 10, 2014 · Abstract:We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two ...
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Our results on various datasets demonstrate that Triple-GAN as a unified model can simultaneously (1) achieve the state-of-the-art classification results among ...
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[PDF] Training Generative Adversarial Networks with Limited Data - NIPS
papers.nips.cc › paper › file
Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge.
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We propose in this paper a novel approach to tackle the problem of mode collapse encountered in generative adversarial network (GAN).
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