生成对抗网络

生成对抗网络（英語：Generative Adversarial Network，简称GAN）是非监督式学习的一种方法，通過两个神经網路相互博弈的方式进行学习。该方法由伊恩·古德费洛等人于2014年提出。^[1] 生成對抗網絡由一個生成網絡與一個判別網絡組成。生成網絡從潛在空間（latent space）中隨機取樣作為輸入，其輸出結果需要盡量模仿訓練集中的真實樣本。判別網絡的輸入則為真實樣本或生成網絡的輸出，其目的是將生成網絡的輸出從真實樣本中盡可能分辨出來。而生成網絡則要盡可能地欺騙判別網絡。兩個網絡相互對抗、不斷調整參數，最終目的是使判別網絡無法判斷生成網絡的輸出結果是否真實。^[2]^[1]^[3]

生成對抗網絡常用於生成以假亂真的圖片。^[4]此外，該方法還被用於生成影片^[5]、三維物體模型^[6]等。

生成對抗網絡虽然最开始提出是為了無監督學習，但经證明對半監督學習（英语：Semi-supervised_learning）^[4]、完全監督學習^[7] 、強化學習^[8]也有效。在2016年的一個研討會上，杨立昆称生成式對抗網絡为「機器學習這二十年來最酷的想法」^[9]。

應用

生成對抗網路的應用範圍正在大幅增加。^[10] ^[11]

時尚和廣告

生成對抗網路可用于创建虚构时装模特的照片，无需聘请模特、摄影师、化妆师，也省下工作室和交通的開銷^[12]。生成對抗網路可用于时尚广告活动，创建來自不同群體的模特兒，这可能会增加這些群體的人的购买意图^[13]。

科學

生成對抗網路可以改善天文图像^[14]，并模拟重力透镜以进行暗物质研究^[15]^[16]^[17]。

在2019年，生成對抗網路成功地模拟了暗物质在太空中特定方向的分布，并预测将要发生的引力透镜。^[18]^[19]

電子遊戲

在2018年，生成對抗網路进入了電子游戏改造社区。對舊的電子游戏透過图像训练，以4k或更高分辨率重新创建低分辨率2D纹理，然后对它们进行下取樣以适应游戏的原始分辨率（结果类似于抗锯齿的超级取樣方法）^[20]。通过适当的训练，生成對抗網路提供更清晰、高于原始的2D纹理图像品質，同时完全保留原始的细节、颜色。

参见

参考文献

^ ^1.0 ^1.1 Goodfellow, Ian J.; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua. Generative Adversarial Networks. 2014. arXiv:1406.2661  [stat.ML].
^ 能根據文字生成圖片的 GAN，深度學習領域的又一新星. [2018-04-15]. （原始内容存档于2018-04-15）.
^ Andrej Karpathy, Pieter Abbeel, Greg Brockman, Peter Chen, Vicki Cheung, Rocky Duan, Ian Goodfellow, Durk Kingma, Jonathan Ho, Rein Houthooft, Tim Salimans, John Schulman, Ilya Sutskever, And Wojciech Zaremba, Generative Models, OpenAI, [2016-04-07], （原始内容存档于2021-04-22）
^ ^4.0 ^4.1 Salimans, Tim; Goodfellow, Ian; Zaremba, Wojciech; Cheung, Vicki; Radford, Alec; Chen, Xi. Improved Techniques for Training GANs. 2016. arXiv:1606.03498  [cs.LG].
^ 存档副本. [2017-03-17]. （原始内容存档于2017-03-20）.
^ 3D Generative Adversarial Network. [2017-03-17]. （原始内容存档于2019-10-27）.
^ Isola, Phillip; Zhu, Jun-Yan; Zhou, Tinghui; Efros, Alexei. Image-to-Image Translation with Conditional Adversarial Nets. Computer Vision and Pattern Recognition. 2017 [2019-06-18]. （原始内容存档于2020-04-14）.
^ Ho, Jonathon; Ermon, Stefano. Generative Adversarial Imitation Learning. Advances in Neural Information Processing Systems. [2019-06-18]. （原始内容存档于2019-10-19）.
^ LeCun, Yann. RL Seminar: The Next Frontier in AI: Unsupervised Learning. [2019-06-18]. （原始内容存档于2020-04-30）.
^ Caesar, Holger, A list of papers on Generative Adversarial (Neural) Networks: nightrome/really-awesome-gan, 2019-03-01 [2019-03-02], （原始内容存档于2020-04-30）
^ 生成式AI：缘起、机遇和挑战, 经济观察报, 2023-01-09. [2023-01-24]. （原始内容存档于2023-01-24）.
^ Wong, Ceecee. The Rise of AI Supermodels. CDO Trends. [2019-06-18]. （原始内容存档于2020-04-16）.
^ Dietmar, Julia. GANs and Deepfakes Could Revolutionize The Fashion Industry. Forbes. [2019-06-18]. （原始内容存档于2019-09-04）.
^ Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan. Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit. Monthly Notices of the Royal Astronomical Society: Letters. 2017-02-01, 467 (1): L110–L114. Bibcode:2017MNRAS.467L.110S. arXiv:1702.00403 . doi:10.1093/mnrasl/slx008.
^ Kincade, Kathy. Researchers Train a Neural Network to Study Dark Matter. R&D Magazine. [2019-06-18]. （原始内容存档于2019-05-15）.
^ Kincade, Kathy. CosmoGAN: Training a neural network to study dark matter. Phys.org. 2019-05-16 [2019-06-18]. （原始内容存档于2020-04-14）.
^ Training a neural network to study dark matter. Science Daily. 2019-05-16 [2019-06-18]. （原始内容存档于2020-04-30）.
^ at 06:13, Katyanna Quach 20 May 2019. Cosmoboffins use neural networks to build dark matter maps the easy way. www.theregister.co.uk. [2019-05-20]. （原始内容存档于2020-04-23）（英语）.
^ Mustafa, Mustafa; Bard, Deborah; Bhimji, Wahid; Lukić, Zarija; Al-Rfou, Rami; Kratochvil, Jan M. CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks. Computational Astrophysics and Cosmology. 2019-05-06, 6 (1): 1. ISSN 2197-7909. doi:10.1186/s40668-019-0029-9.
^ Tang, Xiaoou; Qiao, Yu; Loy, Chen Change; Dong, Chao; Liu, Yihao; Gu, Jinjin; Wu, Shixiang; Yu, Ke; Wang, Xintao. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. 2018-09-01 [2019-06-18]. （原始内容存档于2019-04-13）（英语）.

[MyUser_Arxiv.org_April_7_2016c-1] 1.0 ^1.1 Goodfellow, Ian J.; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua. Generative Adversarial Networks. 2014. arXiv:1406.2661  [stat.ML].

[2] 能根據文字生成圖片的 GAN，深度學習領域的又一新星. [2018-04-15]. （原始内容存档于2018-04-15）.

[OpenAI_com-3] Andrej Karpathy, Pieter Abbeel, Greg Brockman, Peter Chen, Vicki Cheung, Rocky Duan, Ian Goodfellow, Durk Kingma, Jonathan Ho, Rein Houthooft, Tim Salimans, John Schulman, Ilya Sutskever, And Wojciech Zaremba, Generative Models, OpenAI, [2016-04-07], （原始内容存档于2021-04-22）

[ITT_GANs-4] 4.0 ^4.1 Salimans, Tim; Goodfellow, Ian; Zaremba, Wojciech; Cheung, Vicki; Radford, Alec; Chen, Xi. Improved Techniques for Training GANs. 2016. arXiv:1606.03498  [cs.LG].

[5] 存档副本. [2017-03-17]. （原始内容存档于2017-03-20）.

[6] 3D Generative Adversarial Network. [2017-03-17]. （原始内容存档于2019-10-27）.

[7] Isola, Phillip; Zhu, Jun-Yan; Zhou, Tinghui; Efros, Alexei. Image-to-Image Translation with Conditional Adversarial Nets. Computer Vision and Pattern Recognition. 2017 [2019-06-18]. （原始内容存档于2020-04-14）.

[8] Ho, Jonathon; Ermon, Stefano. Generative Adversarial Imitation Learning. Advances in Neural Information Processing Systems. [2019-06-18]. （原始内容存档于2019-10-19）.

[9] LeCun, Yann. RL Seminar: The Next Frontier in AI: Unsupervised Learning. [2019-06-18]. （原始内容存档于2020-04-30）.

[10] Caesar, Holger, A list of papers on Generative Adversarial (Neural) Networks: nightrome/really-awesome-gan, 2019-03-01 [2019-03-02], （原始内容存档于2020-04-30）

[11] 生成式AI：缘起、机遇和挑战, 经济观察报, 2023-01-09. [2023-01-24]. （原始内容存档于2023-01-24）.

[12] Wong, Ceecee. The Rise of AI Supermodels. CDO Trends. [2019-06-18]. （原始内容存档于2020-04-16）.

[13] Dietmar, Julia. GANs and Deepfakes Could Revolutionize The Fashion Industry. Forbes. [2019-06-18]. （原始内容存档于2019-09-04）.

[14] Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan. Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit. Monthly Notices of the Royal Astronomical Society: Letters. 2017-02-01, 467 (1): L110–L114. Bibcode:2017MNRAS.467L.110S. arXiv:1702.00403 . doi:10.1093/mnrasl/slx008.

[15] Kincade, Kathy. Researchers Train a Neural Network to Study Dark Matter. R&D Magazine. [2019-06-18]. （原始内容存档于2019-05-15）.

[16] Kincade, Kathy. CosmoGAN: Training a neural network to study dark matter. Phys.org. 2019-05-16 [2019-06-18]. （原始内容存档于2020-04-14）.

[17] Training a neural network to study dark matter. Science Daily. 2019-05-16 [2019-06-18]. （原始内容存档于2020-04-30）.

[18] t 06:13, Katyanna Quach 20 May 2019. Cosmoboffins use neural networks to build dark matter maps the easy way. www.theregister.co.uk. [2019-05-20]. （原始内容存档于2020-04-23）（英语）.

[19] Mustafa, Mustafa; Bard, Deborah; Bhimji, Wahid; Lukić, Zarija; Al-Rfou, Rami; Kratochvil, Jan M. CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks. Computational Astrophysics and Cosmology. 2019-05-06, 6 (1): 1. ISSN 2197-7909. doi:10.1186/s40668-019-0029-9.

[20] Tang, Xiaoou; Qiao, Yu; Loy, Chen Change; Dong, Chao; Liu, Yihao; Gu, Jinjin; Wu, Shixiang; Yu, Ke; Wang, Xintao. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. 2018-09-01 [2019-06-18]. （原始内容存档于2019-04-13）（英语）.

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