Fast and Accurate ML in 3 Lines of Code

This is an open-source tool to assess and improve the trustworthiness of AI systems.

Model interpretability and understanding for PyTorch

Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition

Code for Guided Evolution for Neural Architecture Search

Code for the paper "Generative Adversarial Graph Convolutional Networks for Human Action Synthesis", WACV 2022

Lime: Explaining the predictions of any machine learning classifier

Synthetic data generation for tabular data

A game theoretic approach to explain the output of any machine learning model.

Code release to accompany paper "Geometry-Aware Gradient Algorithms for Neural Architecture Search."

GraphXAI: Resource to support the development and evaluation of GNN explainers

Code for Gradient-Weighted Class Activation Mapping for Spatio Temporal Graph Convolutional Network

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

BayesGrad: Explaining Predictions of Graph Convolutional Networks

The official project website of "Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition" (The paper of Ske2Grid is published in ICML 2023)

A curated paper list of awesome skeleton-based action recognition.

Official implementation for "InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition"

Yoink from https://github.com/xiaoiker/GCN-NAS

mobilenetv3 with pytorch，provide pre-train model

A paper collection about automated graph learning

Generic Neural Architecture Search via Regression (NeurIPS'21 Spotlight)

A command-line app for converting data files to and from different DJ software formats.

Spatial Temporal Transformer Network for Skeleton-Based Activity Recognition

General implementation of AutoHAS in PyTorch

A curated list of awesome resources combining Transformers with Neural Architecture Search