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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
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