Neural Network Libraries - Examples

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

Docker workflow

• Our Docker workflow offers an easy installation and setup of running environments of our examples.
• See this page.

nnabla-examples-utils (neu)

neu can now be installed as a python package. It provides a wide range of utility functions. For installation and usage, check utils

Interactive Demos

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
SLE-GAN		Image Generation
First Order Motion Model		Facial Motion Transfer
Zooming Slow-Mo		Video Super-Resolution
StyleGAN2		Image Generation
Deep-Exemplar-based-Video-Colorization		Video Colorization
TecoGAN		Video Super-Resolution
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
D3Net		Music Source Separation
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		Visualization
SHAP		Visualization

Fairness of Machine Learning

Name	Notebook	Task	Example
<<<<<<< HEAD
Demographic parity Equal opportunity Equalised odds		[Metrics tutorial] Dataset/Model Bias Check
Reweighing		[Pre-processing tutorial] Dataset/Model Bias Check and Mitigation by Reweighing
GAN Data Debiasing		[Pre-processing tutorial] Dataset/Model Bias Check and Mitigation by GAN
Prejudice Remover Regularizer		[In-processing tutorial] Model Bias Check and Mitigation by Prejudice Removal Technique
<<<<<<< HEAD
=======
Prejudice Remover Regularizer for Images		[In-processing tutorial] Model Bias Check and Mitigation by Prejudice Removal Technique for Images

0f005b62 (update READMEs) Adversarial Debiasing Tutorial | | [In-processing tutorial]
Model Bias Check and Mitigation by Adversarial Debiasing || Adversarial Debiasing for Images| | [In-processing tutorial]
Model Bias Check and Mitigation by Adversarial Debiasing for Images|| Rejection Option based Classification | | [Post-processing tutorial]
Prediction Bias Check and Mitigation by ROC || ======= Metrics tutorial | | Dataset/Model Bias Check || Pre-processing Tutorial | | Dataset/Model Bias Check and Mitigation by Reweighing || Pre-processing Tutorial | | Dataset/Model Bias Check and Mitigation by GAN || In-processing Tutorial | | Model Bias Check and Mitigation by Adversarial Debiasing || In-processing Tutorial | | Model Bias Check and Mitigation by Adversarial Debiasing for Images|| Post-processing Tutorial | | Prediction Bias Check and Mitigation by ROC || Post-processing Tutorial | | Prediction Bias Check and Mitigation by ROC for images || 69a5134a (update readme with collab links) Skin color (Masked Images) | | Facial evaluation for skin color ||

Model Quantization

Name	Notebook	Task	Example
Post-training quantization		Post-training quantization