Theory & Machine Learning
- A Classification-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]
- AdaGAN: Boosting Generative Models [arXiv]
- Adversarial Autoencoders [arXiv]
- Adversarial Discriminative Domain Adaptation [arXiv]
- Adversarial Generator-Encoder Networks [arXiv]
- Adversarial 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]
- Bayesian 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]
- Conditional CycleGAN for Attribute Guided Face Image Generation [arXiv]
- Conditional Generative Adversarial Nets [arXiv] [Code]
- Connecting 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]
- Cooperative 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]
- Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking [arXiv]
- Generalization and Equilibrium in Generative Adversarial Nets (GANs) [arXiv]
- Generating images with recurrent adversarial networks [arXiv]
- Generative Adversarial Networks [arXiv] [Code] [Code]
- Generative Adversarial Networks as Variational Training of Energy Based Models [arXiv]
- Generative Adversarial Networks with Inverse Transformation Unit [arXiv]
- Generative Adversarial Parallelization [arXiv] [Code]
- Generative Adversarial Residual Pairwise Networks for One Shot Learning [arXiv]
- Generative Adversarial Structured Networks [Paper]
- Generative Cooperative Net for Image Generation and Data Augmentation [arXiv]
- Generative Moment Matching Networks [arXiv] [Code]
- Generative 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]
- Improved Techniques 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 Network [Paper]
- It Takes (Only) Two: Adversarial Generator-Encoder Networks [arXiv]
- KGAN: How to Break The Minimax Game in GAN [arXiv]
- Learning 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]
- Learning 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]
- Multi-Agent Diverse Generative Adversarial Networks [arXiv]
- Multi-Generator Gernerative Adversarial Nets [arXiv]
- Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models [arXiv]
- On 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]
- SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [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]
- Stacked Generative Adversarial Networks [arXiv]
- Statistics 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]
- Unrolled Generative Adversarial Networks [arXiv]
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [arXiv] [Code] [Code] [Code] [Code] [Code]
- Wasserstein GAN [arXiv] [Code] [Code]
来源：CSDN
作者：DrogoZhang
链接：https://blog.csdn.net/weixin_40400177/article/details/103537541