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Ian Goodfellow推荐:GAN动物园——GAN的各种变体列表(下载)

时间:2017-04-21 14:10:14来源:新智元mp

原标题:Ian Goodfellow推荐:GAN动物园——GAN的各种变体列表(下载)

新智元编译

来源:deephunt.in

作者: Avinash Hindupur

  【新智元导读】生成对抗网络(GAN)的各种变体非常多,GAN 的发明者 Ian Goodfellow 在Twitter上推荐了这份名为“The GAN Zoo”的各种GAN变体列表,这也表明现在GAN确实非常火,被应用于各种各样的任务。了解这些各种各样的GAN,或许能对你创造自己的 X-GAN有所启发。

  在新智元公众号回复【170421】下载以下全部论文

  几乎每周都有新的关于生成对抗网络(GAN)的论文出现,而且你很难跟踪到它们,因为研究者为这些 GAN 命名的方式非常具有创造性。了解有关 GAN 的更多信息,可以参考 OpenAI 博客的一份非常全面的 GAN 综述文章(地址:https://blog.openai.com/generative-models/),或阅读王飞跃等人的 GAN 综述文章

  这篇文章列举了目前出现的各种GAN变体,并将长期更新。这是一个开源的项目,你也可以通过 pull request 添加作者没有注意到的 GAN,

  GitHub 地址:https://github.com/hindupuravinash/the-gan-zoo

  这份列表的形式是:名称——论文标题(论文均发表在Arxiv,也可在新智元公众号回复【170421】下载)。

  • GAN — Generative Adversarial Networks

  • 3D-GAN — Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

  • AdaGAN— AdaGAN: Boosting Generative Models

  • AffGAN — Amortised MAP Inference for Image Super-resolution

  • ALI — Adversarially Learned Inference

  • AMGAN — Generative Adversarial Nets with Labeled Data by Activation Maximization

  • AnoGAN— Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

  • ArtGAN— ArtGAN: Artwork Synthesis with Conditional Categorial GANs

  • b-GAN— b-GAN: Unified Framework of Generative Adversarial Networks

  • Bayesian GAN— Deep and Hierarchical Implicit Models

  • BEGAN — BEGAN: Boundary Equilibrium Generative Adversarial Networks

  • BiGAN— Adversarial Feature Learning

  • BS-GAN— Boundary-Seeking Generative Adversarial Networks

  • CGAN— Towards Diverse and Natural Image Deions via a Conditional GAN

  • CCGAN— Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

  • CatGAN— Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

  • CoGAN— Coupled Generative Adversarial Networks

  • Context-RNN-GAN— Contextual RNN-GANs for Abstract Reasoning Diagram Generation

  • C-RNN-GAN— C-RNN-GAN: Continuous recurrent neural networks with adversarial training

  • CVAE-GAN— CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

  • CycleGAN— Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

  • DTN — Unsupervised Cross-Domain Image Generation

  • DCGAN— Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

  • DiscoGAN— Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

  • DualGAN— DualGAN: Unsupervised Dual Learning for Image-to-Image Translation

  • EBGAN— Energy-based Generative Adversarial Network

  • f-GAN— f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

  • GoGAN— Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking

  • GP-GAN — GP-GAN: Towards Realistic High-Resolution Image Blending

  • IAN— Neural Photo Editing with Introspective Adversarial Networks

  • iGAN — Generative Visual Manipulation on the Natural Image Manifold

  • IcGAN— Invertible Conditional GANs for image editing

  • ID-CGAN— Image De-raining Using a Conditional Generative Adversarial Network

  • Improved GAN— Improved Techniques for Training GANs

  • InfoGAN— InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

  • LR-GAN— LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation

  • LSGAN — Least Squares Generative Adversarial Networks

  • LS-GAN — Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities

  • MGAN — Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

  • MAGAN— MAGAN: Margin Adaptation for Generative Adversarial Networks

  • MalGAN— Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN

  • MARTA-GAN— Deep Unsupervised Representation Learning for Remote Sensing Images

  • McGAN — McGan: Mean and Covariance Feature Matching GAN

  • MedGAN— Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks

  • MIX+GAN— Generalization and Equilibrium in Generative Adversarial Nets (GANs)

  • MPM-GAN— Message Passing Multi-Agent GANs

  • MV-BiGAN— Multi-view Generative Adversarial Networks

  • pix2pix— Image-to-Image Translation with Conditional Adversarial Networks

  • PPGN — Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

  • PrGAN— 3D Shape Induction from 2D Views of Multiple Objects

  • RenderGAN— RenderGAN: Generating Realistic Labeled Data

  • RTT-GAN— Recurrent Topic-Transition GAN for Visual Paragraph Generation

  • SGAN — Stacked Generative Adversarial Networks

  • SGAN— Texture Synthesis with Spatial Generative Adversarial Networks

  • SAD-GAN — SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks

  • SalGAN— SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

  • SEGAN— SEGAN: Speech Enhancement Generative Adversarial Network

  • SeqGAN— SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

  • SketchGAN — Adversarial Training For Sketch Retrieval

  • SL-GAN — Semi-Latent GAN: Learning to generate and modify facial images from attributes

  • SRGAN — Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

  • S?2;GAN— Generative Image Modeling using Style and Structure Adversarial Networks

  • SSL-GAN— Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

  • StackGAN— StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

  • TGAN— Temporal Generative Adversarial Nets

  • TAC-GAN— TAC-GAN — Text Conditioned Auxiliary Classifier Generative Adversarial Network

  • TP-GAN— Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis

  • Triple-GAN— Triple Generative Adversarial Nets

  • VGAN — Generative Adversarial Networks as Variational Training of Energy Based Models

  • VAE-GAN — Autoencoding beyond pixels using a learned similarity metric

  • ViGAN — Image Generation and Editing with Variational Info Generative AdversarialNetworks

  • WGAN— Wasserstein GAN

  • WGAN-GP— Improved Training of Wasserstein GANs

  • WaterGAN— WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images

  原文地址:https://deephunt.in/the-gan-zoo-79597dc8c347

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