不可錯過的 GAN 資源:教程、視訊、程式碼實現、89 篇論文下載
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NIP 2016 對抗訓練 Workshop
【網頁】https://sites.google.com/site/nips2016adversarial/
【部落格】http://www.inference.vc/my-summary-of-adversarial-training-nips-workshop/
教程 & 部落格
-
【部落格】https://github.com/soumith/ganhacks
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NIPS 2016 教程:生成對抗網路
【arXiv】https://arxiv.org/abs/1701.00160
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深度學習和 GAN 背後的直覺知識——一個基礎理解
【部落格】https://blog.waya.ai/introduction-to-gans-a-boxing-match-b-w-neural-nets-b4e5319cc935
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OpenAI——生成模型
【部落格】https://openai.com/blog/generative-models/
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SimGANs——無監督學習的遊戲規則顛覆者,無人車等
【部落格】https://blog.waya.ai/simgans-applied-to-autonomous-driving-5a8c6676e36b
論文
理論 & 機器學習
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生成對抗網路,逆向強化學習和 Energy-Based 模型之間的聯絡(A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models )
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可擴充套件對抗分類的通用訓練框架(A General Retraining Framework for Scalable Adversarial Classification)
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對抗自編碼器(Adversarial Autoencoders)
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對抗判別的領域適應(Adversarial Discriminative Domain Adaptation)
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對抗性 Generator-Encoder 網路(Adversarial Generator-Encoder Networks)
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對抗特徵學習(Adversarial Feature Learning)
【程式碼】https://github.com/wiseodd/generative-models
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對抗推理學習(Adversarially Learned Inference)
【程式碼】https://github.com/wiseodd/generative-models
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結構化輸出神經網路半監督訓練的一種對抗正則化(An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks)
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聯想式對抗網路(Associative Adversarial Networks)
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b-GAN:生成對抗網路的一個新框架(b-GAN: New Framework of Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
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邊界尋找生成對抗網路(Boundary-Seeking Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
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條件生成對抗網路(Conditional Generative Adversarial Nets)
【程式碼】https://github.com/wiseodd/generative-models
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結合生成對抗網路和 Actor-Critic 方法(Connecting Generative Adversarial Networks and Actor-Critic Methods)
-
描述符和生成網路的協同訓練(Cooperative Training of Deor and Generator Networks)
-
Coupled Generative Adversarial Networks(CoGAN)
【程式碼】https://github.com/wiseodd/generative-models
-
基於能量模型的生成對抗網路(Energy-based Generative Adversarial Network)
【程式碼】https://github.com/wiseodd/generative-models
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對抗樣本的解釋和利用(Explaining and Harnessing Adversarial Examples)
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f-GAN:使用變分發散最小化訓練生成式神經取樣器(f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization)
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Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
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用遞迴對抗網路乘車影象(Generating images with recurrent adversarial networks)
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Generative Adversarial Nets with Labeled Data by Activation Maximization
-
生成對抗網路(Generative Adversarial Networks)
【程式碼】https://github.com/goodfeli/adversarial
【程式碼】https://github.com/wiseodd/generative-models
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生成對抗並行化(Generative Adversarial Parallelization)
【程式碼】https://github.com/wiseodd/generative-models
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One Shot學習的生成性對抗殘差成對網路(Generative Adversarial Residual Pairwise Networks for One Shot Learning)
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生成對抗結構化網路(Generative Adversarial Structured Networks)
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生成式矩匹配網路(Generative Moment Matching Networks)
【程式碼】https://github.com/yujiali/gmmn
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訓練GAN的改進技術(Improved Techniques for Training GANs)
【程式碼】https://github.com/openai/improved-gan
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改善訓練WGAN(Improved Training of Wasserstein GANs)
【程式碼】https://github.com/wiseodd/generative-models
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InfoGAN:通過資訊最大化GAN學習可解釋表示(InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets)
【程式碼】https://github.com/wiseodd/generative-models
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翻轉GAN的生成器(Inverting The Generator Of A Generative Adversarial Network)
-
隱式生成模型裡的學習(Learning in Implicit Generative Models)
-
用GAN學習發現跨域關係(Learning to Discover Cross-Domain Relations with Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
-
最小二乘生成對抗網路,LSGAN(Least Squares Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
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LS-GAN,損失敏感GAN(Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities)
-
LR-GAN:用於影象生成的分層遞迴GAN(LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation)
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MAGAN: Margin Adaptation for Generative Adversarial Networks
【程式碼】https://github.com/wiseodd/generative-models
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最大似然增強的離散生成對抗網路(Maximum-Likelihood Augmented Discrete Generative Adversarial Networks)
-
模式正則化GAN(Mode Regularized Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
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Multi-Agent Diverse Generative Adversarial Networks
-
生成對抗網路中Batch Normalization和Weight Normalization的影響(On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks)
-
基於解碼器的生成模型的定量分析(On the Quantitative Analysis of Decoder-Based Generative Models)
-
SeqGAN:策略漸變的序列生成對抗網路(SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient)
-
深度網路的簡單黑箱對抗干擾(Simple Black-Box Adversarial Perturbations for Deep Networks)
-
Stacked GAN(Stacked Generative Adversarial Networks)
-
通過最大均值差異優化訓練生成神經網路(Training generative neural networks via Maximum Mean Discrepancy optimization)
-
Triple Generative Adversarial Nets
-
Unrolled Generative Adversarial Networks
-
DCGAN無監督表示學習(Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks)
【程式碼】https://github.com/Newmu/dcgan_code
【程式碼】https://github.com/pytorch/examples/tree/master/dcgan
【程式碼】https://github.com/carpedm20/DCGAN-tensorflow
【程式碼】https://github.com/jacobgil/keras-dcgan
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Wasserstein GAN(WGAN)
【程式碼】https://github.com/martinarjovsky/WassersteinGAN
【程式碼】https://github.com/wiseodd/generative-models
視覺應用
-
用對抗網路檢測惡性前列腺癌(Adversarial Networks for the Detection of Aggressive Prostate Cancer)
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條件對抗自編碼器的年齡遞進/迴歸(Age Progression / Regression by Conditional Adversarial Autoencoder)
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ArtGAN:條件分類GAN的藝術作品合成(ArtGAN: Artwork Synthesis with Conditional Categorial GANs)
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Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
-
卷積人臉生成的條件GAN(Conditional generative adversarial nets for convolutional face generation)
-
輔助分類器GAN的條件影象合成(Conditional Image Synthesis with Auxiliary Classifier GANs)
【程式碼】https://github.com/wiseodd/generative-models
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使用對抗網路的Laplacian金字塔的深度生成影象模型(Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks)
【程式碼】https://github.com/facebook/eyescream
【部落格】http://soumith.ch/eyescream/
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Deep multi-scale video prediction beyond mean square error
【程式碼】https://github.com/dyelax/Adversarial_Video_Generation
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DualGAN:影象到影象翻譯的無監督Dual學習(DualGAN: Unsupervised Dual Learning for Image-to-Image Translation)
【程式碼】https://github.com/wiseodd/generative-models
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用迴圈神經網路做全解析度影象壓縮(Full Resolution Image Compression with Recurrent Neural Networks)
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生成以適應:使用GAN對齊域(Generate To Adapt: Aligning Domains using Generative Adversarial Networks)
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生成對抗文字到影象的合成(Generative Adversarial Text to Image Synthesis)
【程式碼】https://github.com/paarthneekhara/text-to-image
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自然影象流形上的生成視覺操作(Generative Visual Manipulation on the Natural Image Manifold)
【專案】http://www.eecs.berkeley.edu/~junyanz/projects/gvm/
【視訊】https://youtu.be/9c4z6YsBGQ0
【程式碼】https://github.com/junyanz/iGAN
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Image De-raining Using a Conditional Generative Adversarial Network
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Image Generation and Editing with Variational Info Generative Adversarial Networks
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用條件對抗網路做 Image-to-Image 翻譯(Image-to-Image Translation with Conditional Adversarial Networks)
【程式碼】https://github.com/phillipi/pix2pix
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用GAN模仿駕駛員行為(Imitating Driver Behavior with Generative Adversarial Networks)
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可逆的條件GAN用於影象編輯(Invertible Conditional GANs for image editing)
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學習驅動模擬器(Learning a Driving Simulator)
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多視角GAN(Multi-view Generative Adversarial Networks)
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利用內省對抗網路編輯圖片(Neural Photo Editing with Introspective Adversarial Networks)
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使用GAN生成照片級真實感的單一影象超解析度(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network)
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Recurrent Topic-Transition GAN for Visual Paragraph Generation
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RenderGAN:生成現實的標籤資料(RenderGAN: Generating Realistic Labeled Data)
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SeGAN: Segmenting and Generating the Invisible
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使用對抗網路做語義分割(Semantic Segmentation using Adversarial Networks)
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半隱性GAN:學習從特徵生成和修改人臉影象(Semi-Latent GAN: Learning to generate and modify facial images from attributes)
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TAC-GAN - 文字條件輔助分類器GAN(TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network)
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通過條件GAN實現多樣化且自然的影象描述(Towards Diverse and Natural Image Deions via a Conditional GAN)
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GAN 提高人的體外識別基線的未標記樣本生成(Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro)
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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無監督異常檢測,用GAN指導標記發現(Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery)
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無監督跨領域影象生成(Unsupervised Cross-Domain Image Generation)
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WaterGAN:實現單目水下影象實時顏色校正的無監督生成網路(WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images)
其他應用
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基於生成模型的文字分類的半監督學習方法(Adversarial Training Methods for Semi-Supervised Text Classification)
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學習在面對對抗性神經網路解密下維護溝通保密性(Learning to Protect Communications with Adversarial Neural Cryptography)
【部落格】http://t.cn/RJitWNw
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MidiNet:利用 1D 和 2D條件實現符號域音樂生成的卷積生成網路(MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions)
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使用生成對抗網路重建三維多孔介質(Reconstruction of three-dimensional porous media using generative adversarial neural networks)
【程式碼】https://github.com/LukasMosser/PorousMediaGan
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Semi-supervised Learning of Compact Document Representations with Deep Networks
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Steganographic GAN(Steganographic Generative Adversarial Networks)
Humor
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停止 GAN 暴力:生成性非對抗網路(Stopping GAN Violence: Generative Unadversarial Networks)
視訊
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Ian Goodfellow:生成對抗網路
【視訊】http://t.cn/RxxJF5A
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Mark Chang:生成對抗網路教程
【視訊】http://t.cn/RXJOKK1
程式碼
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Cleverhans:一個對抗樣本的機器學習庫
【程式碼】https://github.com/openai/cleverhans
【部落格】http://cleverhans.io/
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50行程式碼實現GAN(PyTorch)
【程式碼】https://github.com/devnag/pytorch-generative-adversarial-networks
【部落格】https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
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生成模型集,e.g. GAN, VAE,用 Pytorch 和 TensorFlow 實現
【程式碼】https://github.com/wiseodd/generative-models
【進入新智元微信公眾號,在對話方塊輸入“170501”下載全部 89 篇論文】
原文地址:https://github.com/nightrome/really-awesome-gan
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NIP 2016 對抗訓練 Workshop
【網頁】https://sites.google.com/site/nips2016adversarial/
【部落格】http://www.inference.vc/my-summary-of-adversarial-training-nips-workshop/
教程 & 部落格
-
【部落格】https://github.com/soumith/ganhacks
-
NIPS 2016 教程:生成對抗網路
【arXiv】https://arxiv.org/abs/1701.00160
-
深度學習和 GAN 背後的直覺知識——一個基礎理解
【部落格】https://blog.waya.ai/introduction-to-gans-a-boxing-match-b-w-neural-nets-b4e5319cc935
-
OpenAI——生成模型
【部落格】https://openai.com/blog/generative-models/
-
SimGANs——無監督學習的遊戲規則顛覆者,無人車等
【部落格】https://blog.waya.ai/simgans-applied-to-autonomous-driving-5a8c6676e36b
論文
理論 & 機器學習
-
生成對抗網路,逆向強化學習和 Energy-Based 模型之間的聯絡(A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models )
-
可擴充套件對抗分類的通用訓練框架(A General Retraining Framework for Scalable Adversarial Classification)
-
對抗自編碼器(Adversarial Autoencoders)
-
對抗判別的領域適應(Adversarial Discriminative Domain Adaptation)
-
對抗性 Generator-Encoder 網路(Adversarial Generator-Encoder Networks)
-
對抗特徵學習(Adversarial Feature Learning)
【程式碼】https://github.com/wiseodd/generative-models
-
對抗推理學習(Adversarially Learned Inference)
【程式碼】https://github.com/wiseodd/generative-models
-
結構化輸出神經網路半監督訓練的一種對抗正則化(An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks)
-
聯想式對抗網路(Associative Adversarial Networks)
-
b-GAN:生成對抗網路的一個新框架(b-GAN: New Framework of Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
-
邊界尋找生成對抗網路(Boundary-Seeking Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
-
條件生成對抗網路(Conditional Generative Adversarial Nets)
【程式碼】https://github.com/wiseodd/generative-models
-
結合生成對抗網路和 Actor-Critic 方法(Connecting Generative Adversarial Networks and Actor-Critic Methods)
-
描述符和生成網路的協同訓練(Cooperative Training of Deor and Generator Networks)
-
Coupled Generative Adversarial Networks(CoGAN)
【程式碼】https://github.com/wiseodd/generative-models
-
基於能量模型的生成對抗網路(Energy-based Generative Adversarial Network)
【程式碼】https://github.com/wiseodd/generative-models
-
對抗樣本的解釋和利用(Explaining and Harnessing Adversarial Examples)
-
f-GAN:使用變分發散最小化訓練生成式神經取樣器(f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization)
-
Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
-
用遞迴對抗網路乘車影象(Generating images with recurrent adversarial networks)
-
Generative Adversarial Nets with Labeled Data by Activation Maximization
-
生成對抗網路(Generative Adversarial Networks)
【程式碼】https://github.com/goodfeli/adversarial
【程式碼】https://github.com/wiseodd/generative-models
-
生成對抗並行化(Generative Adversarial Parallelization)
【程式碼】https://github.com/wiseodd/generative-models
-
One Shot學習的生成性對抗殘差成對網路(Generative Adversarial Residual Pairwise Networks for One Shot Learning)
-
生成對抗結構化網路(Generative Adversarial Structured Networks)
-
生成式矩匹配網路(Generative Moment Matching Networks)
【程式碼】https://github.com/yujiali/gmmn
-
訓練GAN的改進技術(Improved Techniques for Training GANs)
【程式碼】https://github.com/openai/improved-gan
-
改善訓練WGAN(Improved Training of Wasserstein GANs)
【程式碼】https://github.com/wiseodd/generative-models
-
InfoGAN:通過資訊最大化GAN學習可解釋表示(InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets)
【程式碼】https://github.com/wiseodd/generative-models
-
翻轉GAN的生成器(Inverting The Generator Of A Generative Adversarial Network)
-
隱式生成模型裡的學習(Learning in Implicit Generative Models)
-
用GAN學習發現跨域關係(Learning to Discover Cross-Domain Relations with Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
-
最小二乘生成對抗網路,LSGAN(Least Squares Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
-
LS-GAN,損失敏感GAN(Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities)
-
LR-GAN:用於影象生成的分層遞迴GAN(LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation)
-
MAGAN: Margin Adaptation for Generative Adversarial Networks
【程式碼】https://github.com/wiseodd/generative-models
-
最大似然增強的離散生成對抗網路(Maximum-Likelihood Augmented Discrete Generative Adversarial Networks)
-
模式正則化GAN(Mode Regularized Generative Adversarial Networks)
【程式碼】https://github.com/wiseodd/generative-models
-
Multi-Agent Diverse Generative Adversarial Networks
-
生成對抗網路中Batch Normalization和Weight Normalization的影響(On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks)
-
基於解碼器的生成模型的定量分析(On the Quantitative Analysis of Decoder-Based Generative Models)
-
SeqGAN:策略漸變的序列生成對抗網路(SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient)
-
深度網路的簡單黑箱對抗干擾(Simple Black-Box Adversarial Perturbations for Deep Networks)
-
Stacked GAN(Stacked Generative Adversarial Networks)
-
通過最大均值差異優化訓練生成神經網路(Training generative neural networks via Maximum Mean Discrepancy optimization)
-
Triple Generative Adversarial Nets
-
Unrolled Generative Adversarial Networks
-
DCGAN無監督表示學習(Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks)
【程式碼】https://github.com/Newmu/dcgan_code
【程式碼】https://github.com/pytorch/examples/tree/master/dcgan
【程式碼】https://github.com/carpedm20/DCGAN-tensorflow
【程式碼】https://github.com/jacobgil/keras-dcgan
-
Wasserstein GAN(WGAN)
【程式碼】https://github.com/martinarjovsky/WassersteinGAN
【程式碼】https://github.com/wiseodd/generative-models
視覺應用
-
用對抗網路檢測惡性前列腺癌(Adversarial Networks for the Detection of Aggressive Prostate Cancer)
-
條件對抗自編碼器的年齡遞進/迴歸(Age Progression / Regression by Conditional Adversarial Autoencoder)
-
ArtGAN:條件分類GAN的藝術作品合成(ArtGAN: Artwork Synthesis with Conditional Categorial GANs)
-
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
-
卷積人臉生成的條件GAN(Conditional generative adversarial nets for convolutional face generation)
-
輔助分類器GAN的條件影象合成(Conditional Image Synthesis with Auxiliary Classifier GANs)
【程式碼】https://github.com/wiseodd/generative-models
-
使用對抗網路的Laplacian金字塔的深度生成影象模型(Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks)
【程式碼】https://github.com/facebook/eyescream
【部落格】http://soumith.ch/eyescream/
-
Deep multi-scale video prediction beyond mean square error
【程式碼】https://github.com/dyelax/Adversarial_Video_Generation
-
DualGAN:影象到影象翻譯的無監督Dual學習(DualGAN: Unsupervised Dual Learning for Image-to-Image Translation)
【程式碼】https://github.com/wiseodd/generative-models
-
用迴圈神經網路做全解析度影象壓縮(Full Resolution Image Compression with Recurrent Neural Networks)
-
生成以適應:使用GAN對齊域(Generate To Adapt: Aligning Domains using Generative Adversarial Networks)
-
生成對抗文字到影象的合成(Generative Adversarial Text to Image Synthesis)
【程式碼】https://github.com/paarthneekhara/text-to-image
-
自然影象流形上的生成視覺操作(Generative Visual Manipulation on the Natural Image Manifold)
【專案】http://www.eecs.berkeley.edu/~junyanz/projects/gvm/
【視訊】https://youtu.be/9c4z6YsBGQ0
【程式碼】https://github.com/junyanz/iGAN
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Image De-raining Using a Conditional Generative Adversarial Network
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Image Generation and Editing with Variational Info Generative Adversarial Networks
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用條件對抗網路做 Image-to-Image 翻譯(Image-to-Image Translation with Conditional Adversarial Networks)
【程式碼】https://github.com/phillipi/pix2pix
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用GAN模仿駕駛員行為(Imitating Driver Behavior with Generative Adversarial Networks)
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可逆的條件GAN用於影象編輯(Invertible Conditional GANs for image editing)
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學習驅動模擬器(Learning a Driving Simulator)
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多視角GAN(Multi-view Generative Adversarial Networks)
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利用內省對抗網路編輯圖片(Neural Photo Editing with Introspective Adversarial Networks)
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使用GAN生成照片級真實感的單一影象超解析度(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network)
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Recurrent Topic-Transition GAN for Visual Paragraph Generation
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RenderGAN:生成現實的標籤資料(RenderGAN: Generating Realistic Labeled Data)
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SeGAN: Segmenting and Generating the Invisible
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使用對抗網路做語義分割(Semantic Segmentation using Adversarial Networks)
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半隱性GAN:學習從特徵生成和修改人臉影象(Semi-Latent GAN: Learning to generate and modify facial images from attributes)
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TAC-GAN - 文字條件輔助分類器GAN(TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network)
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通過條件GAN實現多樣化且自然的影象描述(Towards Diverse and Natural Image Deions via a Conditional GAN)
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GAN 提高人的體外識別基線的未標記樣本生成(Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro)
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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無監督異常檢測,用GAN指導標記發現(Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery)
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無監督跨領域影象生成(Unsupervised Cross-Domain Image Generation)
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WaterGAN:實現單目水下影象實時顏色校正的無監督生成網路(WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images)
其他應用
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基於生成模型的文字分類的半監督學習方法(Adversarial Training Methods for Semi-Supervised Text Classification)
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學習在面對對抗性神經網路解密下維護溝通保密性(Learning to Protect Communications with Adversarial Neural Cryptography)
【部落格】http://t.cn/RJitWNw
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MidiNet:利用 1D 和 2D條件實現符號域音樂生成的卷積生成網路(MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions)
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使用生成對抗網路重建三維多孔介質(Reconstruction of three-dimensional porous media using generative adversarial neural networks)
【程式碼】https://github.com/LukasMosser/PorousMediaGan
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Semi-supervised Learning of Compact Document Representations with Deep Networks
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Steganographic GAN(Steganographic Generative Adversarial Networks)
Humor
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停止 GAN 暴力:生成性非對抗網路(Stopping GAN Violence: Generative Unadversarial Networks)
視訊
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Ian Goodfellow:生成對抗網路
【視訊】http://t.cn/RxxJF5A
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Mark Chang:生成對抗網路教程
【視訊】http://t.cn/RXJOKK1
程式碼
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Cleverhans:一個對抗樣本的機器學習庫
【程式碼】https://github.com/openai/cleverhans
【部落格】http://cleverhans.io/
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50行程式碼實現GAN(PyTorch)
【程式碼】https://github.com/devnag/pytorch-generative-adversarial-networks
【部落格】https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
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生成模型集,e.g. GAN, VAE,用 Pytorch 和 TensorFlow 實現
【程式碼】https://github.com/wiseodd/generative-models
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原文地址:https://github.com/nightrome/really-awesome-gan