997篇-历史最全生成对抗网络（GAN）论文串烧

    什么是GAN？

    GAN（Generative Adversarial Netwo，生成对抗网络）是用于无监督学习的机器学习模型，由Ian Goodfellow等人在2014年提出，由神经网络构成判别器和生成器构成，通过一种互相竞争的机制组成的一种学习框架。

    卷积神经网络之父-Yann LeCun这样评论GAN：

    在我看来，最重要的是对抗训练( GAN也称为生成对抗网络)。这一想法最初是Ian Goodfellow在蒙特利尔大学读书是提出的，他当时是Yoshua Bengio的学生(Yoshua Bengio先加入了Google Brain，最近有离职加入OpenAI )。在我看来，这一想法与正在被提出的各种变化，是最近十年来在ML中最有趣的想法。 

    GAN是一个非常强大的框架，这里，我们主要整理了自2014年，GAN推出以来，一些优质的论文，分享给有需要的朋友。

    （限于篇幅原因，本文主要列出前50篇GAN相关论文，文末附上完整且带论文链接的list）

列表如下：

1.    3C-GAN: AN CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY

    2018/1 ICLR2018 3C-GAN

    

2.    3D conditional generative adversarial networks for high-quality PET image estimation at low dose  

    2018/7 Medical: Reconstruction New

    

3.    3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation 

    2018/3 New, 3D

    

4.    3D Medical Image Synthesis using Generative Adversarial Networks 

    2017/ Medical: Synthersize Medical

    

5.    3D Object Reconstruction from a Single Depth View with Adversarial Learning?  

    2017/8 Applied Vision 3D-RecGAN

    

6.    3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversary Network 

    2017/11 ORGAN

    

8.    3D Shape Induction from 2D Views of Multiple Objects 

    2016/12 3D Object generation PrGAN Citation: 9

    

9.    3D-Scene-GAN: Three-dimensional Scene Reconstruction with Generative Adversarial Networks 

    2018/2 ICLR2018

    

10.    A Classification-Based Perspective on GAN Distributions 

    2018/1 ICLR2018

    

11.    A Classification-Based Perspective on GAN Distributions? 

    2017/11 Theory & Machine Learning Citation: 1

    

12.    A conditional adversarial network for semantic segmentation of brain tumor 

    2018/2 Medical: Segmentation New

    

13.    A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models? 

    2016/11 Theory & Machine Learning Citation: 16

    

14.    A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation 

    2018/1 None

    

15.    A Deep Predictive Coding Network for Learning Latent Representations 

    2018/3 New, Bio

    

16.    A General Retraining Framework for Scalable Adversarial Classification? 

    2016/4 Theory & Machine Learning Citation: 6

    

17.    A Generalized Active Learning Approach for Unsupervised Anomaly Detection 

    2018/5 New

    

18.    A generative adversarial framework for positive-unlabeled classification 

    2017/11 GPU

    

19.    A Generative Model for Volume Rendering 

    2017/10 Medical: Volume Rendering Applied Other

    

20.    A Hybrid Model for Identity Obfuscation by Face Replacement 

    2018/4 New

    

21.    A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation 

    2018/4 New

    

22.    A Novel Approach to Artistic Textual Visualization via GAN? 

    2017/10 Applied Vision GAN-ATV

    

23.    A Self-Training Method for Semi-Supervised GANs 

    2018/1 ICLR2018

    

24.    A Solvable High-Dimensional Model of GAN 

    2018/5 New

    

25.    A step towards procedural terrain generation with GANs? 

    2017/7 Applied Vision

    

26.    A Study into the similarity in generator and discriminator in GAN architecture 

    2018/2 None

    

27.    A Study of Cross-domain Generative Models applied to Cartoon Series? 

    2017/9

    

28.    A survey of image synthesis and editing with generative adversarial networks 

    2017/12 Medical: Synthersize New

    

29.    A Variational Inequality Perspective on Generative Adversarial Nets 

    2018/2 None

    

30.    A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection? 

    2017/4 Object Detection Citation: 12

    

31.    ABC-GAN: Adaptive Blur and Control for improved training stability of Generative Adversarial Networks 

    2017/8 ABC-GAN Stars: 2

    

32.    Abnormal Event Detection in Videos using Generative Adversarial Nets? 

    2017/8 Applied Vision

    

33.    Accelerated Magnetic Resonance Imaging by Adversarial Neural Network 

    2017/9 Medical: Reconstruction

    

34.    Accelerating Science with?Generative?Adversarial?Networks: An Application to 3D Particle Showers in Multilayer Calorimeters  

    2018/1 Medical: Reconstruction New

    

35.    Activation Maximization Generative Adversarial Nets 

    2017/3 Theory & Machine Learning AM-GAN

    

36.    Activation Maximization Generative Adversarial Nets 

    2018/1 ICLR2018

    

37.    ACtuAL: Actor-Critic Under Adversarial Learning 

    2017/11 ACtuAL

    

38.    AdaGAN: Boosting Generative Models? 

    2017/1 Ensemble, Theory & Machine Learning AdaGAN Citation: 19

    

39.    Adaptive template generation for amyloid PET using a deep learning approach  

    2018/5 Medical: Synthersize New

    

40.    Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks? 

    2017/9

    

41.    Adversarial Attacks on Neural Network Policies? 

    2017/2 Citation: 21

    

42.    Adversarial Autoencoders?  

    2015/11 Theory & Machine Learning AAE Citation: 163 Stars: 130

    

43.    Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data 

    2018/3 New

    

44.    Adversarial Deep Structural Networks for Mammographic Mass Segmentation 

    2016/12 BioarXiv

    

45.    Adversarial Deep Structural Networks for Mammographic Mass Segmentation? 

    2016/12 Semantic Segmentation Citation: 7

    

46.    Adversarial Deep Structured Nets for Mass Segmentation from Mammograms  

     2017/10 Medical: Segmentation Stars: 13

    

47.    Adversarial Discriminative Domain Adaptation? 

    2017/2 Theory & Machine Learning Citation: 52

    

48.    Adversarial examples for generative models 

    2017/2 Adversarial Examples (Defense vs Attack)

    

49.    Adversarial Examples for Semantic Segmentation and Object Detection? 

    2017/7 Citation: 17

    

50.    Adversarial Examples Generation and Defense Based on Generative Adversarial Network 

    2017/ Adversarial Examples (Defense vs Attack)

完整论文列表下载地址

    链接:         https://pan.baidu.com/s/1KM1a17VZ2UrmClNCLLjChg 

    密码: y6a6

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