原始GAN

Goodfellow和Bengio等人發表在NIPS 2014年的文章Generative adversary network，是生成對抗網絡的開創文章，論文思想啟發自博弈論中的二人零和博弈。在二人零和博弈中，兩位博弈方的利益之和為零或一個常數，即一方有所得，另一方必有所失。GAN模型中的兩位博弈方分別由生成式模型(generative model)和判別式模型(discriminative model)充當。生成模型G捕捉樣本數據的分布，判別模型D是一個二分類器，估計一個樣本來自于訓練數據（而非生成數據）的概率。G和D一般都是非線性映射函數，例如多層感知機、卷積神經網絡等。

如圖所示，左圖是一個判別式模型，當輸入訓練數據x時，期待輸出高概率（接近1）；右圖下半部分是生成模型，輸入是一些服從某一簡單分布（例如高斯分布）的隨機噪聲z，輸出是與訓練圖像相同尺寸的生成圖像。向判別模型D輸入生成樣本，對于D來說期望輸出低概率（判斷為生成樣本），對于生成模型G來說要盡量欺騙D，使判別模型輸出高概率（誤判為真實樣本），從而形成競爭與對抗。

GAN.png

GAN優勢很多：根據實際的結果，看上去產生了更好的樣本；GAN能訓練任何一種生成器網絡；GAN不需要設計遵循任何種類的因式分解的模型，任何生成器網絡和任何鑒別器都會有用；GAN無需利用馬爾科夫鏈反復采樣，無需在學習過程中進行推斷，回避了近似計算棘手的概率的難題。

GAN主要存在的以下問題：網絡難以收斂，目前所有的理論都認為GAN應該在納什均衡上有很好的表現，但梯度下降只有在凸函數的情況下才能保證實現納什均衡。

GAN發展

一方面GAN的發展很快，這里只是簡單粗略將相關論文分了幾類，歡迎反饋，持續更新。此外最近ICLR 2017 在進行Open Review，可以關注下ICLR 2017 Conference Track，也有相應論文筆記分享ICLR 2017 | GAN Missing Modes 和 GAN

GAN從2014年到現在發展很快，特別是最近ICLR 2016/2017關于GAN的論文很多，GAN現在有很多問題還有到解決，潛力很大。總體可以將已有的GANs論文分為以下幾類

GAN Theory

GAN in Semi-supervised

Muti-GAN

GAN with other Generative model

GAN with RNN

GAN in Application

GAN Theory

此類關注與無監督GAN本身原理的研究：比較兩個分布的距離；用DL的一些方法讓GAN快速收斂等等。相關論文有：

• GAN: Goodfellow, Ian, et al. "Generative adversarial nets."?Advances in Neural Information Processing Systems. 2014.
• LAPGAN: Denton, Emily L., Soumith Chintala, and Rob Fergus. "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks."?Advances in neural information processing systems. 2015.
• DCGAN: Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks."?arXiv preprint arXiv:1511.06434?(2015).
• Improved GAN: Salimans, Tim, et al. "Improved techniques for training gans."?arXiv preprint arXiv:1606.03498?(2016).
• InfoGAN: Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets."?arXiv preprint arXiv:1606.03657(2016).**
• EnergyGAN: Zhao, Junbo, Michael Mathieu, and Yann LeCun. "Energy-based Generative Adversarial Network."?arXiv preprint arXiv:1609.03126?(2016).
• Creswell, Antonia, and Anil A. Bharath. "Task Specific Adversarial Cost Function."?arXiv preprint arXiv:1609.08661?(2016).
• f-GAN: Nowozin, Sebastian, Botond Cseke, and Ryota Tomioka. "f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization."?arXiv preprint arXiv:1606.00709?(2016).
• Unrolled Generative Adversarial Networks, ICLR 2017 Open Review
• Improving Generative Adversarial Networks with Denoising Feature Matching, ICLR 2017 Open Review
• Mode Regularized Generative Adversarial Networks, ICLR 2017 Open Review
• b-GAN: Unified Framework of Generative Adversarial Networks, ICLR 2017 Open Review
• Mohamed, Shakir, and Balaji Lakshminarayanan. "Learning in Implicit Generative Models."?arXiv preprint arXiv:1610.03483?(2016).

GAN in Semi-supervised

此類研究將GAN用于半監督學習，相關論文有：

• Springenberg, Jost Tobias. "Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks."?arXiv preprint arXiv:1511.06390?(2015).
• Odena, Augustus. "Semi-Supervised Learning with Generative Adversarial Networks."?arXiv preprint arXiv:1606.01583?(2016).

Muti-GAN

此類研究將多個GAN進行組合，相關論文有：

• CoupledGAN: Liu, Ming-Yu, and Oncel Tuzel. "Coupled Generative Adversarial Networks."?arXiv preprint arXiv:1606.07536?(2016).
• Wang, Xiaolong, and Abhinav Gupta. "Generative Image Modeling using Style and Structure Adversarial Networks."?arXiv preprint arXiv:1603.05631(2016).
• Generative Adversarial Parallelization, ICLR 2017 Open Review
• LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation, ICLR 2017 Open Review

GAN with other Generative model

此類研究將GAN與其他生成模型組合，相關論文有：

• Dosovitskiy, Alexey, and Thomas Brox. "Generating images with perceptual similarity metrics based on deep networks."?arXiv preprint arXiv:1602.02644(2016).
• Larsen, Anders Boesen Lindbo, S?ren Kaae S?nderby, and Ole Winther. "Autoencoding beyond pixels using a learned similarity metric."?arXiv preprint arXiv:1512.09300?(2015).
• Theis, Lucas, and Matthias Bethge. "Generative image modeling using spatial lstms."?Advances in Neural Information Processing Systems. 2015.

GAN with RNN

此類研究將GAN與RNN結合（也以參考Pixel RNN），相關論文有：

• Im, Daniel Jiwoong, et al. "Generating images with recurrent adversarial networks."?arXiv preprint arXiv:1602.05110?(2016).
• Kwak, Hanock, and Byoung-Tak Zhang. "Generating Images Part by Part with Composite Generative Adversarial Networks."?arXiv preprint arXiv:1607.05387?(2016).
• Yu, Lantao, et al. "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient."?arXiv preprint arXiv:1609.05473?(2016).

GAN in Application

此類研究將GAN的實際運用（不包括圖像生成），相關論文有：

• Zhu, Jun-Yan, et al. "Generative visual manipulation on the natural image manifold."?European Conference on Computer Vision. Springer International Publishing, 2016.
• Creswell, Antonia, and Anil Anthony Bharath. "Adversarial Training For Sketch Retrieval."?European Conference on Computer Vision. Springer International Publishing, 2016.
• Reed, Scott, et al. "Generative adversarial text to image synthesis."?arXiv preprint arXiv:1605.05396?(2016).
• Ravanbakhsh, Siamak, et al. "Enabling Dark Energy Science with Deep Generative Models of Galaxy Images."?arXiv preprint arXiv:1609.05796(2016).
• Abadi, Martín, and David G. Andersen. "Learning to Protect Communications with Adversarial Neural Cryptography."?arXiv preprint arXiv:1610.06918(2016).
• Odena, Augustus, Christopher Olah, and Jonathon Shlens. "Conditional Image Synthesis With Auxiliary Classifier GANs."?arXiv preprint arXiv:1610.09585?(2016).
• Ledig, Christian, et al. "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network."?arXiv preprint arXiv:1609.04802?(2016).
• Nguyen, Anh, et al. "Synthesizing the preferred inputs for neurons in neural networks via deep generator networks."?arXiv preprint arXiv:1605.09304(2016).

原文地址： http://www.jianshu.com/p/2acb804dd811

與50位技術專家面對面20年技術見證，附贈技術全景圖

總結

以上是生活随笔為你收集整理的GAN论文整理的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。