https://github.com/cbfinn/maml
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Papers with Code 아카이브
Category-Agnostic Pose Estimation
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Category-Agnostic Pose Estimation (CAPE) aims to create a pose estimation model capable of detecting the pose of any class of object given only a few samples with keypoint definition.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cbfinn/maml_rl
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/chenbys/fmmp
공식 코드Recurrent Feature Mining and Keypoint Mixup Padding for Category-Agnostic Pose Estimation
https://github.com/chenbys/metapoint
공식 코드Meta-Point Learning and Refining for Category-Agnostic Pose Estimation
https://github.com/jakesnell/prototypical-networks
공식 코드https://github.com/luminxu/pose-for-everything
공식 코드Pose for Everything: Towards Category-Agnostic Pose Estimation
https://github.com/matanr/capex
공식 코드CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation
https://github.com/orhir/EdgeCape
공식 코드Edge Weight Prediction For Category-Agnostic Pose Estimation
https://github.com/orhir/PoseAnything
공식 코드A Graph-Based Approach for Category-Agnostic Pose Estimation
https://github.com/tiny-smart/SCAPE
공식 코드https://github.com/2023-MindSpore-4/Code-5/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ArnoutDevos/maml-cifar-fs
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/BBDrive/Meta-SGD-RL
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/COAOX/Cifar-Prototypical
https://github.com/CocoJam/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/DrMMZ/ProtoNet
https://github.com/GeorgeDUT/MetaRLSAS
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/HenryCWoo/LTLProject
https://github.com/HilbertXu/MAML-Tensorflow
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Hsankesara/DeepResearch
https://github.com/JWSoh/MZSR
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/JeonMinkyu/MAML_Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/KamalM8/Few-Shot-learning-Fashion
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/LKLQQ/LEO
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Michedev/Prototypical-Networks
https://github.com/Mind23-2/MindCode-101/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-3/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-55
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/MoritzTaylor/maml-rl-tf2
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Octavio-Pappalardo/MAML_for_RL_pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/RongKaiWeskerMA/INSTA
https://github.com/Runist/torch_maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Sha-Lab/FEAT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.