GANs學習系列(5): 生成式對抗網路Generative Adversarial Networks
阿新 • • 發佈：2019-02-04
【前言】
本文首先介紹生成式模型，然後著重梳理生成式模型（Generative Models）中生成對抗網路（Generative Adversarial Network）的研究與發展。作者按照GAN主幹論文、GAN應用性論文、GAN相關論文分類整理了45篇近兩年的論文，著重梳理了主幹論文之間的聯絡與區別，揭示生成式對抗網路的研究脈絡。
涉及的論文有：
[1] Goodfellow Ian, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems. 2014: 2672-2680.
[2] Mirza M, Osindero S. Conditional Generative Adversarial Nets[J]. Computer Science, 2014:2672-2680.
[3] Denton E L, Chintala S, Fergus R. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks[C]//Advances in neural information processing systems. 2015: 1486-1494.
[4] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.
[5] Im D J, Kim C D, Jiang H, et al. Generating images with recurrent adversarial networks[J]. arXiv preprint arXiv:1602.05110, 2016.
[6] Larsen A B L, Sønderby S K, Winther O. Autoencoding beyond pixels using a learned similarity metric[J]. arXiv preprint arXiv:1512.09300, 2015.
[7] Wang X, Gupta A. Generative Image Modeling using Style and Structure Adversarial Networks[J]. arXiv preprint arXiv:1603.05631, 2016.
[8] Chen X, Duan Y, Houthooft R, et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets[J]. arXiv preprint arXiv:1606.03657, 2016.
[9] Kurakin A, Goodfellow I, Bengio S. Adversarial examples in the physical world[J]. arXiv preprint arXiv:1607.02533, 2016.
[10] Odena A. Semi-Supervised Learning with Generative Adversarial Networks[J]. arXiv preprint arXiv:1606.01583, 2016.
[11] Springenberg J T. Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks[J]. arXiv preprint arXiv:1511.06390, 2015.
本文首先介紹生成式模型，然後著重梳理生成式模型（Generative Models）中生成對抗網路（Generative Adversarial Network）的研究與發展。作者按照GAN主幹論文、GAN應用性論文、GAN相關論文分類整理了45篇近兩年的論文，著重梳理了主幹論文之間的聯絡與區別，揭示生成式對抗網路的研究脈絡。
涉及的論文有：
[1] Goodfellow Ian, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems. 2014: 2672-2680.
[2] Mirza M, Osindero S. Conditional Generative Adversarial Nets[J]. Computer Science, 2014:2672-2680.
[3] Denton E L, Chintala S, Fergus R. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks[C]//Advances in neural information processing systems. 2015: 1486-1494.
[4] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.
[5] Im D J, Kim C D, Jiang H, et al. Generating images with recurrent adversarial networks[J]. arXiv preprint arXiv:1602.05110, 2016.
[6] Larsen A B L, Sønderby S K, Winther O. Autoencoding beyond pixels using a learned similarity metric[J]. arXiv preprint arXiv:1512.09300, 2015.
[7] Wang X, Gupta A. Generative Image Modeling using Style and Structure Adversarial Networks[J]. arXiv preprint arXiv:1603.05631, 2016.
[8] Chen X, Duan Y, Houthooft R, et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets[J]. arXiv preprint arXiv:1606.03657, 2016.
[9] Kurakin A, Goodfellow I, Bengio S. Adversarial examples in the physical world[J]. arXiv preprint arXiv:1607.02533, 2016.
[10] Odena A. Semi-Supervised Learning with Generative Adversarial Networks[J]. arXiv preprint arXiv:1606.01583, 2016.
[11] Springenberg J T. Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks[J]. arXiv preprint arXiv:1511.06390, 2015.