This repository contains working examples of Neural Network Libraries. Before running any of the examples in this repository, you must install the Python package for Neural Network Libraries. The Python install guide can be found here.
Before running an example, also run the following command inside the example directory, to install additional dependencies:
cd example_directory
pip install -r requirements.txt
- Our Docker workflow offers an easy installation and setup of running environments of our examples.
- See this page.
We have prepared interactive demos, where you can play around without having to worry about the codes and the internal mechanism. You can run it directly on Colab from the links in the table below.
Vision: Generation, Enhancement, Animation
| Name | Notebook | Task | Example |
|---|---|---|---|
| First Order Motion Model |  |
Facial Motion Transfer |  |
| Zooming Slow-Mo | |
Video Super-Resolution |   |
| StyleGAN2 | |
Image Generation |  |
| ESR-GAN | |
Super-Resolution |   |
| Self-Attention GAN | |
Image Generation | |
| StarGAN | |
Image Translation |   |
| DCGAN | |
Image Generation |
Vision: Recognition
| Name | Notebook | Task | Example |
|---|---|---|---|
| CenterNet | |
Object Detection |  |
| PSMNet | |
Stereo Depth Estimation |   |
| Face Alignment Network | |
Facial Keypoint Detection |   |
| YOLO v2 | |
Object Detection | |
| ResNet/ResNeXt/SENet | |
Image Classification |  |
Audio
| Name | Notebook | Task | Example |
|---|---|---|---|
| X-UMX | |
Music Source Separation |  |
Machine Learning
| Name | Notebook | Task | Example |
|---|---|---|---|
| Out-of-Core training | |
Out-of-Core training |  |
| MixUp / CutMix / VH-Mixup | |
Data Augmentation |   |
| Virtual Adversarial Training | |
Semi-Supervised Learning | |
| SiameseNet | |
Feature Embedding | |
| Variational Auto-encoder | |
Unsupervised Learning |
eXplainable AI
| Name | Notebook | Task | Example |
|---|---|---|---|
| Grad-CAM | |
Grad-CAM |  |