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generative_model [2018/07/26 09:11]
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generative_model [2019/01/13 20:06] (current)
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 In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation. We also show evidence that this strategy may be broadly applicable to improving GAN training in other data modalities. In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation. We also show evidence that this strategy may be broadly applicable to improving GAN training in other data modalities.
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 +https://​lilianweng.github.io/​lil-log/​2017/​08/​20/​from-GAN-to-WGAN.html From GAN to WGAN
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 +https://​arxiv.org/​abs/​1809.02145v1 GANs beyond divergence minimization
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 + These results suggest that GANs do not conform well to the divergence minimization theory and form a much broader range of models than previously assumed.
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 +https://​openreview.net/​pdf?​id=B1xsqj09Fm LARGE SCALE GAN TRAINING FOR
 +HIGH FIDELITY NATURAL IMAGE SYNTHESIS
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 +https://​arxiv.org/​abs/​1810.09136v1 Do Deep Generative Models Know What They Don't Know?
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 +https://​ieeexplore.ieee.org/​stamp/​stamp.jsp?​arnumber=8520899 https://​github.com/​ToniCreswell/​InvertingGAN Inverting the Generator of a Generative Adversarial Network
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 +https://​arxiv.org/​abs/​1811.03259 Bias and Generalization in Deep Generative Models: An Empirical Study
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 +https://​arxiv.org/​abs/​1807.06358v2 IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
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 +https://​openreview.net/​pdf?​id=HJxB5sRcFQ LAYOUTGAN: GENERATING GRAPHIC LAYOUTS
 +WITH WIREFRAME DISCRIMINATORS