997篇-历史最全生成对抗网络(GAN)论文串烧
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什么是GAN?(本文内容整理自网络)
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
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3C-GAN: AN CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY 2018/1 ICLR2018 3C-GAN
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3D conditional generative adversarial networks for high-quality PET image estimation at low dose
2018/7 Medical: Reconstruction New
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3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
2018/3 New, 3D
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3D Medical Image Synthesis using Generative Adversarial Networks
2017/ Medical: Synthersize Medical
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3D Object Reconstruction from a Single Depth View with Adversarial Learning?
2017/8 Applied Vision 3D-RecGAN
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3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversary Network
2017/11 ORGAN
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3D Shape Induction from 2D Views of Multiple Objects
2016/12 3D Object generation PrGAN Citation: 9
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3D-Scene-GAN: Three-dimensional Scene Reconstruction with Generative Adversarial Networks
2018/2 ICLR2018
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A Classification-Based Perspective on GAN Distributions
2018/1 ICLR2018
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A Classification-Based Perspective on GAN Distributions?
2017/11 Theory & Machine Learning Citation: 1
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A conditional adversarial network for semantic segmentation of brain tumor
2018/2 Medical: Segmentation New
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A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models?
2016/11 Theory & Machine Learning Citation: 16
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A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation
2018/1 None
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A Deep Predictive Coding Network for Learning Latent Representations
2018/3 New, Bio
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A General Retraining Framework for Scalable Adversarial Classification?
2016/4 Theory & Machine Learning Citation: 6
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A Generalized Active Learning Approach for Unsupervised Anomaly Detection
2018/5 New
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A generative adversarial framework for positive-unlabeled classification
2017/11 GPU
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A Generative Model for Volume Rendering
2017/10 Medical: Volume Rendering Applied Other
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A Hybrid Model for Identity Obfuscation by Face Replacement
2018/4 New
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A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation
2018/4 New
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A Novel Approach to Artistic Textual Visualization via GAN?
2017/10 Applied Vision GAN-ATV
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A Self-Training Method for Semi-Supervised GANs
2018/1 ICLR2018
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A Solvable High-Dimensional Model of GAN
2018/5 New
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A step towards procedural terrain generation with GANs?
2017/7 Applied Vision
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A Study into the similarity in generator and discriminator in GAN architecture
2018/2 None
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A Study of Cross-domain Generative Models applied to Cartoon Series?
2017/9
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A survey of image synthesis and editing with generative adversarial networks
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A Variational Inequality Perspective on Generative Adversarial Nets
2018/2 None
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A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection?
2017/4 Object Detection Citation: 12
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ABC-GAN: Adaptive Blur and Control for improved training stability of Generative Adversarial Networks
2017/8 ABC-GAN Stars: 2
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Abnormal Event Detection in Videos using Generative Adversarial Nets?
2017/8 Applied Vision
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Accelerated Magnetic Resonance Imaging by Adversarial Neural Network
2017/9 Medical: Reconstruction
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Accelerating Science with?Generative?Adversarial?Networks: An Application to 3D Particle Showers in Multilayer Calorimeters
2018/1 Medical: Reconstruction New
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Activation Maximization Generative Adversarial Nets
2017/3 Theory & Machine Learning AM-GAN
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Activation Maximization Generative Adversarial Nets
2018/1 ICLR2018
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ACtuAL: Actor-Critic Under Adversarial Learning
2017/11 ACtuAL
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AdaGAN: Boosting Generative Models?
2017/1 Ensemble, Theory & Machine Learning AdaGAN Citation: 19
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Adaptive template generation for amyloid PET using a deep learning approach
2018/5 Medical: Synthersize New
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Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks?
2017/9
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Adversarial Attacks on Neural Network Policies?
2017/2 Citation: 21
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Adversarial Autoencoders?
2015/11 Theory & Machine Learning AAE Citation: 163 Stars: 130
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Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data
2018/3 New
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Adversarial Deep Structural Networks for Mammographic Mass Segmentation
2016/12 BioarXiv
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Adversarial Deep Structural Networks for Mammographic Mass Segmentation?
2016/12 Semantic Segmentation Citation: 7
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Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
2017/10 Medical: Segmentation Stars: 13
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Adversarial Discriminative Domain Adaptation?
2017/2 Theory & Machine Learning Citation: 52
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Adversarial examples for generative models
2017/2 Adversarial Examples (Defense vs Attack)
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Adversarial Examples for Semantic Segmentation and Object Detection?
2017/7 Citation: 17
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Adversarial Examples Generation and Defense Based on Generative Adversarial Network
2017/ Adversarial Examples (Defense vs Attack)
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