GAN Theory :
1. Energy-based generative adversarial network Paper Code.
2. Mode Regularized Generative Adversarial Networks Paper.
3. Improving Generative Adversarial Networks with Denoising Feature Matching Paper Code.
4. How to train Gans Docu.
5. Towards Principled Methods for Training Generative Adversarial Networks Paper.
6. Unrolled Generative Adversarial Networks Paper Code.
7. Least Squares Generative Adversarial Networks Paper Code.
8. Wasserstein GAN Paper Code.
9. Improved Training of Wasserstein GANs Paper Code.
10. Generalization and Equilibrium in Generative Adversarial Nets Paper.
Object Detection :
1. Perceptual generative adversarial networks for small object detection Paper.
2. A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection Paper code.
Semantic Segmentation :
1. Adversarial Deep Structural Networks for Mammographic Mass Segmentation Paper code.
2. Semantic Segmentation using Adversarial Networks Paper.
Unclassified :
1. Adversarial Autoencoders Paper Code.
2. Generating images with recurrent adversarial networks Paper Code.
3. Generative Image Modeling using Style and Structure Adversarial Networks Paper Code.
4. Adversarial Feature Learning Paper.
Tutorial :
1. http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)
2. PDF (NIPS Lecun Slides)
3. ICCV 2017 Tutorial About GANS
| 
