Collection of Jupyter Notebooks with Apache MXNet GLUON implementation of various standard GAN papers.
Note: At this point of time, this repo may be used as reference implementations only. Hyperparameters, and various other factors are not fully tuned. Accuracies/any metric claimed in paper may NOT be exactly reflected with this implementations.
pip install mxnet-mkl # For CPU machines
pip install mxnet-cu90mkl # For GPU machines
| No. | Title | Paper | Code |
|---|---|---|---|
| 1 | Generative Adversarial Networks (GAN) | https://arxiv.org/abs/1406.2661 | GAN |
| 2 | Deep Convolutional GAN (DCGAN) | https://arxiv.org/abs/1511.06434 | TODO |
| 3 | Conditional GAN (CGAN) | https://arxiv.org/abs/1411.1784 | TODO |
| 4 | Pix2Pix | https://arxiv.org/abs/1611.07004 | TODO |
| 5 | BEGAN: Boundary Equilibrium Generative Adversarial Networks | https://arxiv.org/abs/1703.10717 | TODO |
| 6 | BicycleGAN: Toward Multimodal Image-to-Image Translation | https://arxiv.org/abs/1711.11586 | TODO |
| 7 | Boundary seeking GAN | https://arxiv.org/abs/1611.06430 | TODO |
| 8 | Context Conditional GAN | https://arxiv.org/abs/1611.06430 | TODO |
| 9 | Context Encoders: Feature Learning by Inpainting | https://arxiv.org/abs/1604.07379 | TODO |
| 10 | Coupled Generative Adversarial Networks | https://arxiv.org/abs/1606.07536 | TODO |
| 11 | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks | https://arxiv.org/abs/1703.10593 | TODO |
| 12 | DiscoGAN - Learning to Discover Cross-Domain Relations with Generative Adversarial Networks | https://arxiv.org/abs/1703.05192 | TODO |
| 13 | DualGAN: Unsupervised Dual Learning for Image-to-Image Translation | https://arxiv.org/abs/1703.05192 | TODO |
| 14 | Energy-based Generative Adversarial Network | https://arxiv.org/abs/1609.03126 | TODO |
| 15 | InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets | https://arxiv.org/abs/1606.03657 | TODO |
| 16 | Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks | https://arxiv.org/abs/1612.05424 | TODO |
| 17 | StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation | https://arxiv.org/abs/1711.09020 | TODO |
| 18 | SRGAN: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | https://arxiv.org/abs/1609.04802 | TODO |
| 19 | Improved Training of Wasserstein GANs | https://arxiv.org/abs/1704.00028 | TODO |
All contributions welcome! You can contribute with bug fixes, changes, suggestions for new paper implementations. Please see above list of GAN networks I plan to implement, please feel free to create an issue and assign yourself any implementation.
See this issue with list of TODOs enhancements planned - #1
- This work is heavily motivated and influenced from this awesome project - https://github.com/eriklindernoren/keras-gan