GAN论文,理论以及机器学习x

　GAN 论文 理论以及机器学习 Theory & Machine Learning lassification-Based Perspective on GAN Distributions [arXiv] • A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models [arXiv] A General Retraining Framework for Scalable Adversarial Classification [Paper] Activation Maximization Generative Adversarial Nets [arXiv] aGAN: Boosting Generative Models [arXiv] Autoencoders [arXiv] Discriminative Domain Adaptation [arXiv] Generator-Encoder Networks [arXiv] Feature Learning [arXiv] [Code] Adversarially Learned Inference [arXiv] [Code] AE-GAN: adversarial eliminating with GAN [arXiv] • An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks [arXiv] APE-GAN: Adversarial Perturbation Elimination with GAN [arXiv] Associative Adversarial Networks [arXiv] Autoencoding beyond pixels using a learned similarity metric [arXiv] Conditional Generative Adverserial Networks [arXiv] Bayesian GAN [arXiv] • BEGAN: Boundary Equilibrium Generative Adversarial Networks [Paper] [arXiv] [Code] Binary Generative Adversarial Networks for Image Retrieval [arXiv] Boundary-Seeking Generative Adversarial Networks [arXiv] [Code] • CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training [arXiv] Class-Splitting Generative Adversarial Networks [arXiv] • Comparison of Maximum Likelihood and GAN-based training of Real NVPs [arXiv] CycleGAN for Attribute Guided Face Image Generation [arXiv] ditional Generative Adversarial Nets [arXiv] [Code] necting Generative Adversarial Networks and Actor-Critic Methods [Paper] Continual Learning in Generative Adversarial Nets [arXiv] • C-RNN-GAN: Continuous recurrent neural networks with adversarial training [arXiv] • CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning [arXiv]

　operative Training of Descriptor and Generator Networks [arXiv] Coupled Generative Adversarial Networks [arXiv] [Code] Dualing GANs [arXiv] Deep and Hierarchical Implicit Models [arXiv] Energy-based Generative Adversarial Network [arXiv] [Code] Explaining and Harnessing Adversarial Examples [arXiv] • Flow-GAN: Bridging implicit and prescribed learning in generative models [arXiv] • f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization [arXiv] [Code] • Gg of GANs: Generative Adversarial Networks with Maximum Margin Ranking [arXiv] lization and Equilibrium in Generative Adversarial Nets (GANs) [arXiv] ng images with recurrent adversarial networks [arXiv] [arXiv] [Code] [Code] • Generative Adversarial Networks as Variational Training of Energy Based Models [arXiv] Networks with Inverse Transformation Unit [arXiv] Parallelization [arXiv] [Code] • Generative Adversarial Residual Pairwise Networks for One Shot Learning [arXiv] Adversarial Structured Networks [Paper] • Generative Cooperative Net for Image Generation and Data Augmentation [arXiv] Moment Matching Networks [arXiv] [Code] nerative Semantic Manipulation with Contrasting GAN [arXiv] Geometric GAN [arXiv] Good Semi-supervised Learning that Requires a Bad GAN [arXiv] Gradient descent GAN optimization is locally stable [arXiv] How to Train Your DRAGAN [arXiv] • Image Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks [arXiv] • Improved Semi-supervised Learning with GANs using Manifold Invariances [arXiv] echniques for Training GANs [arXiv] [Code] Improved Training of Wasserstein GANs [arXiv] [Code] • InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets [arXiv] [Code] Inverting The Generator Of A Generative Adversarial [Paper] It Takes (Only) Two: Adversarial Generator-Encoder Networks [arXiv] • KGAN: How to Break The Minimax Game in GAN [arXiv]

　in Implicit Generative Models [Paper] • Learning Loss for Knowledge Distillation with Conditional Adversarial Networks [arXiv] • Learning to Discover Cross-Domain Relations with Generative Adversarial Networks [arXiv] [Code] rning Texture Manifolds with the Periodic Spatial GAN [arXiv] Least Squares Generative Adversarial Networks [arXiv] [Code] Linking Generative Adversarial Learning and Binary Classification [arXiv] Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities [arXiv] • LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation [arXiv] MAGAN: Margin Adaptation for Generative Adversarial Networks [arXiv] [Code] • Maximum-Likelihood Augmented Discrete Generative Adversarial Networks [arXiv] McGan: Mean and Covariance Feature Matching GAN [arXiv] Message Passing Multi-Agent GANs [arXiv] • MMD GAN: Towards Deeper Understanding of Moment Matching Network [arXiv] Mode Regularized Generative Adversarial Networks [arXiv] [Code] Agent Diverse Generative Adversarial Networks [arXiv] Multi-Generator Gernerative Adversarial Nets [arXiv] • Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models [arXiv] Convergence and Stability of GANs [arXiv] • On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks [arXiv] On the Quantitative Analysis of Decoder-Based Generative Models [arXiv] Optimizing the Latent Space of Generative Networks [arXiv] Parametrizing filters of a CNN with a GAN [arXiv] PixelGAN Autoencoders [arXiv] • Progressive Growing of GANs for Improved Quality, Stability, and Variation [arXiv] [Code] • AN: Adversarial Network with Multi-scale L1 Loss for Medical Image gmentation [arXiv] SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient [arXiv] Simple Black-Box Adversarial Perturbations for Deep Networks [Paper] Softmax GAN [arXiv] • Stabilizing Training of Generative Adversarial Networks through Regularization [arXiv] cked Generative Adversarial Networks [arXiv] atistics of Deep Generated Images [arXiv] • Structured Generative Adversarial Networks [arXiv]

　Tensorizing Generative Adversarial Nets [arXiv] The Cramer Distance as a Solution to Biased Wasserstein Gradients [arXiv] • Towards Understanding Adversarial Learning for Joint Distribution Matching [arXiv] • Training generative neural networks via Maximum Mean Discrepancy optimization [arXiv] Triple Generative Adversarial Nets [arXiv] rolled Generative Adversarial Networks [arXiv] • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [arXiv] [Code] [Code] [Code] [Code] [Code] • Wasserstein GAN [arXiv] [Code] [Code]

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