生成对抗网络 (Generative Adversarial Network, GAN) 是一类神经网络,通过轮流训练判别器 (Discriminator) 和生成器 (Generator),令其相互对抗,来从复杂概率分布中采样,例如生成图片、文字、语音等。GAN 最初由 Ian Goodfellow 提出,原论文见 Generative Adversarial Networks

知识荟萃

生成对抗网络(GAN)专知荟萃

一、理论学习

  1. 训练GANs的技巧    
    

参考链接:[http://papers.nips.cc/paper/6124-improved-techniques-for-training-gans.pdf] 2. Energy-Based GANs 以及Yann Le Cun 的相关研究
参考链接:[http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf] 3. 模式正则化GAN
参考链接:[https://arxiv.org/pdf/1612.02136.pdf] 4. 最新NIPS2016也有最新的关于训练GAN模型的总结
参考链接:[https://github.com/soumith/ganhacks] 5. The GAN Zoo千奇百怪的生成对抗网络,都在这里了。你没看错,里面已经有有近百个了。
参考链接: [https://github.com/hindupuravinash/the-gan-zoo]

二、综述

1.中科院自动化所 中文综述 《生成式对抗网络 GAN 的研究进展与展望》
参考链接:[https://pan.baidu.com/s/1dEMITo9] 密码: qqcc

三、报告

  1. Ian Goodfellow的GANs报告ICCV 2017
    参考链接:[https://pan.baidu.com/s/1bpIZvfL]
  2. Ian Goodfellow的GANs报告ICCV 2017的中文讲稿
    参考链接:[https://mp.weixin.qq.com/s/nPBFrnO3_QJjAzm37G5ceQ]
  3. Ian Goodfellow的GANs报告NIPS 2016
    参考链接:[http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf]
  4. Ian Goodfellow的GANs报告NIPS 2016 的中文讲稿
    参考链接:[http://www.sohu.com/a/121189842_465975]
  5. Russ Salakhutdinov的深度生成模型
    参考链接:[http://www.cs.toronto.edu/~rsalakhu/talk_Montreal_2016_Salakhutdinov.pdf]

四、教程

  1. NIPS 2016教程:生成对抗网络     
    

参考链接:[https://arxiv.org/pdf/1701.00160.pdf] 2. 训练GANs的技巧和窍门
参考链接:[https://github.com/soumith/ganhacks] 3. OpenAI生成模型
参考链接:[https://blog.openai.com/generative-models/] 4. 用Keras实现MNIST生成对抗模型
参考链接:[https://oshearesearch.com/index.PHP/2016/07/01/mnist-generative-adversarial-model-in-keras/] 5. 用深度学习TensorFlow实现图像修复
参考链接:[http://bamos.github.io/2016/08/09/deep-completion/]

五、中文博客资料

1.生成对抗网络初学入门:一文读懂GAN的基本原理
[http://www.xtecher.com/Xfeature/view?aid=7496]
2.深入浅出:GAN原理与应用入门介绍
[https://zhuanlan.zhihu.com/p/28731033]
3.港理工在读博士李嫣然深入浅出GAN之应用篇
参考链接:链接: [https://pan.baidu.com/s/1o8n4UDk] 密码: 78wt
4.萌物生成器:如何使用四种GAN制造猫图
参考链接:[https://zhuanlan.zhihu.com/p/27769807]
5.GAN学习指南:从原理入门到制作生成Demo
参考链接:[https://zhuanlan.zhihu.com/p/24767059x]
6.生成式对抗网络GAN研究进展
参考链接:[http://blog.csdn.net/solomon1558/article/details/52537114]
7.生成对抗网络(GAN)的前沿进展(论文、报告、框架和Github资源)汇总
参考链接:[http://blog.csdn.net/love666666shen/article/details/74953970]

六、Github资源以及模型

  1. 深度卷积生成对抗模型(DCGAN)   
    

参考链接:[https://github.com/Newmu/dcgan_code]
2. TensorFlow实现深度卷积生成对抗模型(DCGAN)
参考链接:[https://github.com/carpedm20/DCGAN-tensorflow]
3. Torch实现深度卷积生成对抗模型(DCGAN)
参考链接:[https://github.com/soumith/dcgan.torch]
4. Keras实现深度卷积生成对抗模型(DCGAN)
参考链接:[https://github.com/jacobgil/keras-dcgan]
5. 使用神经网络生成自然图像(Facebook的Eyescream项目)
参考链接:[https://github.com/facebook/eyescream]
6. 对抗自编码(AdversarialAutoEncoder)
参考链接:[https://github.com/musyoku/adversarial-autoencoder]
7. 利用ThoughtVectors 实现文本到图像的合成
参考链接:[https://github.com/paarthneekhara/text-to-image]
8. 对抗样本生成器(Adversarialexample generator)
参考链接:[https://github.com/e-lab/torch-toolbox/tree/master/Adversarial]
9. 深度生成模型的半监督学习
参考链接:[https://github.com/dpkingma/nips14-ssl]
10. GANs的训练方法
参考链接:[https://github.com/openai/improved-gan]
11. 生成式矩匹配网络(Generative Moment Matching Networks, GMMNs)
参考链接:[https://github.com/yujiali/gmmn]
12. 对抗视频生成
参考链接:[https://github.com/dyelax/Adversarial_Video_Generation]
13. 基于条件对抗网络的图像到图像翻译(pix2pix)
参考链接:[https://github.com/phillipi/pix2pix]
14. 对抗机器学习库Cleverhans,
参考链接:[https://github.com/openai/cleverhans]

七、最新研究论文

2014

  1. 对抗实例的解释和利用(Explaining and Harnessing Adversarial Examples)2014
    原文链接:[https://arxiv.org/pdf/1412.6572.pdf]
  2. 基于深度生成模型的半监督学习( Semi-Supervised Learning with Deep Generative Models )2014
    原文链接:[https://arxiv.org/pdf/1406.5298v2.pdf]
  3. 条件生成对抗网络(Conditional Generative Adversarial Nets)2014
    原文链接:[https://arxiv.org/pdf/1411.1784v1.pdf]

2015

  1. 基于深度卷积生成对抗网络的无监督学习(Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (DCGANs))2015
    原文链接:[https://arxiv.org/pdf/1511.06434v2.pdf]
  2. 基于拉普拉斯金字塔生成式对抗网络的深度图像生成模型(Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks)2015
    原文链接:[http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-5. laplacian-pyramid-of-adversarial-networks.pdf]
  3. 生成式矩匹配网络(Generative Moment Matching Networks)2015
    原文链接:[http://proceedings.mlr.press/v37/li15.pdf]
  4. 超越均方误差的深度多尺度视频预测(Deep multi-scale video prediction beyond mean square error)2015
    原文链接:[https://arxiv.org/pdf/1511.05440.pdf]
  5. 通过学习相似性度量的超像素自编码(Autoencoding beyond pixels using a learned similarity metric)2015
    原文链接:[https://arxiv.org/pdf/1512.09300.pdf]
  6. 对抗自编码(Adversarial Autoencoders)2015
    原文链接:[https://arxiv.org/pdf/1511.05644.pdf]
  7. 基于像素卷积神经网络的条件生成图片(Conditional Image Generation with PixelCNN Decoders)2015
    原文链接:[https://arxiv.org/pdf/1606.05328.pdf]
  8. 通过平均差异最大优化训练生成神经网络(Training generative neural networks via Maximum Mean Discrepancy optimization)2015
    原文链接:[https://arxiv.org/pdf/1505.03906.pdf]

2016

  1. 训练GANs的一些技巧(Improved Techniques for Training GANs)2016
    原文链接:[https://arxiv.org/pdf/1606.03498v1.pdf]
  2. InfoGAN:基于信息最大化GANs的可解释表达学习(InfoGAN:Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets)2016
    原文链接:[https://arxiv.org/pdf/1606.03657v1.pdf]
    3.上下文像素编码:通过修复进行特征学习(Context Encoders: Feature Learning by Inpainting)2016 原文链接: [http://www.cvfoundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf]
  3. 生成对抗网络实现文本合成图像(Generative Adversarial Text to Image Synthesis)2016
    原文链接:[http://proceedings.mlr.press/v48/reed16.pdf]
  4. 对抗特征学习(Adversarial Feature Learning)2016
    原文链接:[https://arxiv.org/pdf/1605.09782.pdf]
  5. 结合逆自回归流的变分推理(Improving Variational Inference with Inverse Autoregressive Flow )2016
    原文链接: [https://papers.nips.cc/paper/6581-improving-variational-autoencoders-with-inverse-autoregressive-flow.pdf]
  6. 深度学习系统对抗样本黑盒攻击(Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples)2016
    原文链接:[https://arxiv.org/pdf/1602.02697.pdf]
  7. 参加,推断,重复:基于生成模型的快速场景理解(Attend, infer, repeat: Fast scene understanding with generative models)2016
    原文链接:[https://arxiv.org/pdf/1603.08575.pdf]
  8. f-GAN: 使用变分散度最小化训练生成神经采样器(f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization )2016
    原文链接: [http://papers.nips.cc/paper/6066-tagger-deep-unsupervised-perceptual-grouping.pdf]
    10.在自然图像流形上的生成视觉操作(Generative Visual Manipulation on the Natural Image Manifold)2016
    原文链接:[https://arxiv.org/pdf/1609.03552.pdf]
  9. 对抗性推断学习(Adversarially Learned Inference)2016
    原文链接:[https://arxiv.org/pdf/1606.00704.pdf]
  10. 基于循环对抗网络的图像生成(Generating images with recurrent adversarial networks)2016
    原文链接:[https://arxiv.org/pdf/1602.05110.pdf]
  11. 生成对抗模仿学习(Generative Adversarial Imitation Learning)2016
    原文链接:[http://papers.nips.cc/paper/6391-generative-adversarial-imitation-learning.pdf]
  12. 基于3D生成对抗模型学习物体形状的概率隐空间(Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling)2016
    原文链接:[https://arxiv.org/pdf/1610.07584.pdf]
  13. 学习画画(Learning What and Where to Draw)2016
    原文链接:[https://arxiv.org/pdf/1610.02454v1.pdf]
  14. 基于辅助分类器GANs的条件图像合成(Conditional Image Synthesis with Auxiliary Classifier GANs)2016
    原文链接:[https://arxiv.org/pdf/1610.09585.pdf]
  15. 隐生成模型的学习(Learning in Implicit Generative Models)2016
    原文:[https://arxiv.org/pdf/1610.03483.pdf]
  16. VIME: 变分信息最大化探索(VIME: Variational Information Maximizing Exploration)2016
    原文链接: [http://papers.nips.cc/paper/6591-vime-variational-information-maximizing-exploration.pdf]
  17. 生成对抗网络的展开(Unrolled Generative Adversarial Networks)2016
    原文链接:[https://arxiv.org/pdf/1611.02163.pdf]
  18. 基于内省对抗网络的神经图像编辑(Neural Photo Editing with Introspective Adversarial Networks)2016
    原文链接:[https://arxiv.org/pdf/1609.07093.pdf]
  19. 基于解码器的生成模型的定量分析(On the Quantitative Analysis of Decoder-Based Generative Models )2016
    原文链接:[https://arxiv.org/pdf/1611.04273.pdf]
  20. 结合生成对抗网络和Actor-Critic 方法(Connecting Generative Adversarial Networks and Actor-Critic Methods)2016
    原文链接:[https://arxiv.org/pdf/1610.01945.pdf]
  21. 通过对抗网络使用模拟和非监督图像训练( Learning from Simulated and Unsupervised Images through Adversarial Training)2016
    原文链接:[https://arxiv.org/pdf/1612.07828.pdf]
  22. 基于上下文RNN-GANs的抽象推理图的生成(Contextual RNN-GANs for Abstract Reasoning Diagram Generation)2016
    原文链接:[https://arxiv.org/pdf/1609.09444.pdf]
  23. 生成多对抗网络(Generative Multi-Adversarial Networks)2016
    原文链接:[https://arxiv.org/pdf/1611.01673.pdf]
  24. 生成对抗网络组合(Ensembles of Generative Adversarial Network)2016
    原文链接:[https://arxiv.org/pdf/1612.00991.pdf]
  25. 改进生成器目标的GANs(Improved generator objectives for GANs) 2016
    原文链接:[https://arxiv.org/pdf/1612.02780.pdf]

2017

  1. 训练生成对抗网络的基本方法(Towards Principled Methods for Training Generative Adversarial Networks)2017
    原文链接:[https://arxiv.org/pdf/1701.04862.pdf]
  2. 生成对抗模型的隐向量精准修复(Precise Recovery of Latent Vectors from Generative Adversarial Networks)2017
    原文链接:[https://openreview.NET/pdf?id=HJC88BzFl]
  3. 生成混合模型(Generative Mixture of Networks)2017
    原文链接:[https://arxiv.org/pdf/1702.03307.pdf]
  4. 记忆生成时空模型(Generative Temporal Models with Memory)2017
    原文链接:[https://arxiv.org/pdf/1702.04649.pdf]
  5. 止GAN暴力:生成性非对抗模型(Stopping GAN Violence: Generative Unadversarial Networks)2017
    原文链接:[https://arxiv.org/pdf/1703.02528.pdf]
  6. 贝叶斯GANs:贝叶斯与GAN结合(Bayesian GAN) 原文链接:https://arxiv.org/abs/1705.09558

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生成对抗网络(GAN)已经在计算机视觉、自然语言处理等领域推广了各种应用,因为它的生成模型能够从现有的样本分布中合理地生成真实的例子。GAN不仅在基于数据生成的任务上提供了令人印象深刻的性能,而且由于其博弈优化策略,也为面向隐私和安全的研究提供了有利条件。遗憾的是,目前并没有对GAN在隐私和安全方面进行全面的综述,这也促使了本文对这些最新的研究成果进行系统的总结。现有的作品根据隐私和安全功能进行适当的分类,并对其优缺点进行综合分析。鉴于GAN在隐私和安全方面仍处于非常初级的阶段,并提出了有待解决的独特挑战,本文还阐述了GAN在隐私和安全方面的一些潜在应用,并阐述了未来的一些研究方向。

生成对抗网络(Generative Adversarial Networks, GAN)带来的技术突破迅速对机器学习及其相关领域产生了革命性的影响,这种影响已经蔓延到各个研究领域和应用领域。作为一种强大的生成框架,GAN显著促进了许多复杂任务的应用,如图像生成、超分辨率、文本数据操作等。最近,利用GAN为严重的隐私和安全问题制定优雅的解决方案,由于其博弈优化策略,在学术界和业界都变得越来越流行。本综述的目的是提供一个关于GAN的全面的回顾和深入总结的最新技术,并讨论了一些GAN在隐私和安全领域有前途的未来研究方向。我们以对GAN的简要介绍开始我们的综述。

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In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by internal status/tissue changes as different modalities are usually obtained at a different time. This paper proposes a novel deep learning method, Structure-aware Generative Adversarial Network (SA-GAN), that preserves the shapes and locations of in-consistent structures when generating medical images. SA-GAN is employed to generate synthetic computed tomography (synCT) images from magnetic resonance imaging (MRI) with two parallel streams: the global stream translates the input from the MRI to the CT domain while the local stream automatically segments the inconsistent organs, maintains their locations and shapes in MRI, and translates the organ intensities to CT. Through extensive experiments on a pelvic dataset, we demonstrate that SA-GAN provides clinically acceptable accuracy on both synCTs and organ segmentation and supports MR-only treatment planning in disease sites with internal organ status changes.

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In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by internal status/tissue changes as different modalities are usually obtained at a different time. This paper proposes a novel deep learning method, Structure-aware Generative Adversarial Network (SA-GAN), that preserves the shapes and locations of in-consistent structures when generating medical images. SA-GAN is employed to generate synthetic computed tomography (synCT) images from magnetic resonance imaging (MRI) with two parallel streams: the global stream translates the input from the MRI to the CT domain while the local stream automatically segments the inconsistent organs, maintains their locations and shapes in MRI, and translates the organ intensities to CT. Through extensive experiments on a pelvic dataset, we demonstrate that SA-GAN provides clinically acceptable accuracy on both synCTs and organ segmentation and supports MR-only treatment planning in disease sites with internal organ status changes.

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