生成对抗网络(GAN)是近段时间以来最受研究者关注的机器学习方法之一,深度学习泰斗 Yann LeCun 就曾多次谈到 这种机器学习理念的巨大价值和未来前景。在本文中,机器之心总结了 GitHub 上两篇关于 GAN 的资源,其中一篇介绍了 GAN 的一些引人关注的新理论和实践(如 Wasserstein GAN),另一篇则集中展示了大量 GAN 相关的论文。
以下是两篇原文的链接:
GAN 理论&实践的新进展:https://casmls.github.io/general/2017/04/13/gan.html
GAN 论文列表项目:https://github.com/nightrome/really-awesome-gan
GAN 理论&实践的新进展
首先我们看看 Liping Liu 在 github.io 上发布的这篇介绍了 GAN 理论和实践上的新进展的文章。这篇文章对两篇 GAN 相关的论文进行了探讨;其中第一篇是 Arora et al. 的《Generalization and Equilibrium in Generative Adversarial Nets》,该论文是一篇对 GAN 的理论研究;第二篇则是 Gulrajani et al. 的《Improved Training of Wasserstein GANs》,其介绍了一种用于 Facebook 最近提出并引起了广泛关注的 Wasserstein GAN 的新训练方法。下面的视频对第一篇论文给出了很好的介绍:
视频内容
GAN 和 Wasserstein GAN
GAN 训练是一个两方博弈过程,其中生成器(generator)的目标是最小化其生成的分布和数据分布之间的差异,而判别器(discriminator)的工作则是尽力区分生成器分布的样本和真实数据分布的样本。当判别器的表现不比随机乱猜更好时,我们就认为生成器「获胜」了。
On the intuition behind deep learning & GANs—towards a fundamental understanding [https://blog.waya.ai/introduction-to-gans-a-boxing-match-b-w-neural-nets-b4e5319cc935]
SimGANs - a game changer in unsupervised learning, self driving cars, and more [https://blog.waya.ai/simgans-applied-to-autonomous-driving-5a8c6676e36b]
论文
理论和机器学习
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models [https://arxiv.org/abs/1611.03852]
A General Retraining Framework for Scalable Adversarial Classification [https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_2.pdf]
b-GAN: New Framework of Generative Adversarial Networks [https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_4.pdf]
Generative Moment Matching Networks [https://arxiv.org/abs/1502.02761] [https://github.com/yujiali/gmmn]
Improved Techniques for Training GANs [https://arxiv.org/abs/1606.03498] [https://github.com/openai/improved-gan]
Inverting The Generator Of A Generative Adversarial Network [https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_9.pdf]
Learning in Implicit Generative Models [https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_10.pdf]
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks [https://arxiv.org/abs/1703.05192]
Least Squares Generative Adversarial Networks [https://arxiv.org/abs/1611.04076]
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities [https://arxiv.org/abs/1701.06264]
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation [https://arxiv.org/abs/1703.01560]
On the Quantitative Analysis of Decoder-Based Generative Models [https://arxiv.org/abs/1611.04273]
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient [https://arxiv.org/abs/1609.05473]
Simple Black-Box Adversarial Perturbations for Deep Networks [https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_11.pdf]
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [https://arxiv.org/abs/1511.06434] [https://github.com/Newmu/dcgan_code] [https://github.com/pytorch/examples/tree/master/dcgan][https://github.com/carpedm20/DCGAN-tensorflow] [https://github.com/soumith/dcgan.torch] [https://github.com/jacobgil/keras-dcgan]
Wasserstein GAN [https://arxiv.org/abs/1701.07875] [https://github.com/martinarjovsky/WassersteinGAN]
视觉应用
Adversarial Networks for the Detection of Aggressive Prostate Cancer [https://arxiv.org/abs/1702.08014]
Age Progression / Regression by Conditional Adversarial Autoencoder [https://arxiv.org/abs/1702.08423]
ArtGAN: Artwork Synthesis with Conditional Categorial GANs [https://arxiv.org/abs/1702.03410]
Conditional generative adversarial nets for convolutional face generation [http://www.foldl.me/uploads/2015/conditional-gans-face-generation/paper.pdf]
Conditional Image Synthesis with Auxiliary Classifier GANs [https://arxiv.org/abs/1610.09585]
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks [https://arxiv.org/abs/1506.05751] [https://github.com/facebook/eyescream] [http://soumith.ch/eyescream/]
Deep multi-scale video prediction beyond mean square error [https://arxiv.org/abs/1511.05440] [https://github.com/dyelax/Adversarial_Video_Generation]
Full Resolution Image Compression with Recurrent Neural Networks [https://arxiv.org/abs/1608.05148]
Generate To Adapt: Aligning Domains using Generative Adversarial Networks [https://arxiv.org/pdf/1704.01705.pdf]
Generative Adversarial Text to Image Synthesis [https://arxiv.org/abs/1605.05396] [https://github.com/paarthneekhara/text-to-image]
Generative Visual Manipulation on the Natural Image Manifold [http://www.eecs.berkeley.edu/~junyanz/projects/gvm/] [https://youtu.be/9c4z6YsBGQ0] [https://arxiv.org/abs/1609.03552] [https://github.com/junyanz/iGAN]
Image De-raining Using a Conditional Generative Adversarial Network [https://arxiv.org/abs/1701.05957]
Image Generation and Editing with Variational Info Generative Adversarial Networks [https://arxiv.org/abs/1701.04568]
Image-to-Image Translation with Conditional Adversarial Networks [https://arxiv.org/abs/1611.07004] [https://github.com/phillipi/pix2pix]
Imitating Driver Behavior with Generative Adversarial Networks [https://arxiv.org/abs/1701.06699]
Invertible Conditional GANs for image editing [https://arxiv.org/abs/1611.06355]
WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images [https://arxiv.org/abs/1702.07392]
其它应用
Adversarial Training Methods for Semi-Supervised Text Classification [https://arxiv.org/abs/1605.07725]
Learning to Protect Communications with Adversarial Neural Cryptography [https://arxiv.org/abs/1610.06918] [https://blog.acolyer.org/2017/02/10/learning-to-protect-communications-with-adversarial-neural-cryptography/]
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions [https://arxiv.org/abs/1703.10847]
Semi-supervised Learning of Compact Document Representations with Deep Networks [http://www.cs.nyu.edu/~ranzato/publications/ranzato-icml08.pdf]
Generative Adversarial Networks by Ian Goodfellow [https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Generative-Adversarial-Networks]
Tutorial on Generative Adversarial Networks by Mark Chang [https://www.youtube.com/playlist?list=PLeeHDpwX2Kj5Ugx6c9EfDLDojuQxnmxmU]
代码
Cleverhans: A library for benchmarking vulnerability to adversarial examples [https://github.com/openai/cleverhans] [http://cleverhans.io/]
Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch) [https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f] [https://github.com/devnag/pytorch-generative-adversarial-networks]