https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Papers with Code 아카이브
3D Action Recognition
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- 평가표
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Image: Rahmani et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jacobgil/pytorch-grad-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/jbloomaus/saelens
Sparse Autoencoders Find Highly Interpretable Features in Language Models
https://github.com/jhchang/DFDC
https://github.com/jiangboahu/glcn-tf
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/joaopedromattos/gnee
https://github.com/jtpils/treegan
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/kchengiva/Shift-GCN
https://github.com/ken724049/action-recognition
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/kenziyuliu/DGNN-PyTorch
https://github.com/kiharalab/gnn_pocket
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/l13025816/PGCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/lab-midas/lapnet
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/leo-liuzy/codeupdatearena
CodeUpdateArena: Benchmarking Knowledge Editing on API Updates
https://github.com/lingzhang1/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/lingzhang1/pointnet_tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/lipingcoding/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/lisssse14/Grad_CAM_PLUS_PLUS
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/liu6zijian/simplified-gcn-model
https://github.com/liuch37/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/liujf69/TD-GCN-Gesture
https://github.com/llm-editing/editing-attack
https://github.com/lmnt-com/haste
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/lukecavabarrett/pna
https://github.com/m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/mHealthBuet/SegCodeNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/magnux/DMNN
https://github.com/mahendrathapa/graph-convolution-network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/GKD
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/RA-GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/marble0117/GNN_models_pytorch
https://github.com/marble0117/GNN_models_pytorch_geometric
https://github.com/marblet/GNN_models_pytorch_geometric
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.