GAN对抗神经网络简介.pdf

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文本预览

MedGAN b-GAN LSGAN ID-CGAN LS-GAN AﬀGAN CoGAN LAPGAN LR-GAN DiscoGAN AMGAN CGAN MPM-GAN iGAN IcGAN AdaGAN IAN InfoGAN Generative Adversarial Networks CatGAN Ian Goodfellow, Staﬀ Research Scientist, Google Brain alpha-GAN FF-GAN C-RNN-GAN ICCV Tutorial on GANs Venice, 2017-10-22 GoGAN GMAN MIX+GAN BS-GAN DR-GAN AC-GAN DCGAN McGAN MGAN C-VAE-GAN MAGAN GAWWN CCGAN 3D-GAN DualGAN Bayesian GAN EBGAN ALI MARTA-GAN WGAN-GP Context-RNN-GAN f-GAN ArtGAN CycleGAN AnoGAN MAD-GAN BEGAN MalGAN BiGAN GP-GAN DTN AL-CGAN

Generative Modeling • Density estimation • Sample generation Training examples Model samples (Goodfellow 2017)

Maximum Likelihood BRIEF ARTICLE THE AUTHOR ✓⇤ = arg max ✓ Ex⇠pdata log pmodel(x | ✓) (Goodfellow 2017)

Adversarial Nets Framework D(x) tries to be near 1 Diﬀerentiable function D D tries to make D(G(z)) near 0, G tries to make D(G(z)) near 1 D x sampled from data x sampled from model (Goodfellow et al., 2014) Diﬀerentiable function G Input noise z (Goodfellow 2017)

What can you do with GANs? • Simulated environments and training data • Missing data • Semi-supervised learning • Multiple correct answers • Realistic generation tasks • Simulation by prediction • Solve inference problems • Learn useful embeddings (Goodfellow 2017)

(Goodfellow 2017)

GANs for simulated training data (Shrivastava et al., 2016) (Goodfellow 2017)

GANs for domain adaptation (Bousmalis et al., 2016) (Raﬀel, 2017)

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GAN对抗神经网络简介.pdf

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