https://github.com/gebob19/cscd94-metalearning
Model-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/gebob19/cscd94_metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/goldblum/AdversarialQuerying
https://github.com/hfahrudin/reptile_implement_tf2
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/hoyeoplee/pytorch-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/hrdwsong/ProtoNet-Paddle
https://github.com/iclavera/meta-mb
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ido90/roml-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/infinitemugen/MAML-Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/jonasrothfuss/promp
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/joshfp/fellowship.ai
https://github.com/joshfp/one-shot-learning
https://github.com/jsalbert/prototypical-networks
https://github.com/katerakelly/pytorch-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/laiviet/maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/learnables/learn2learn
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/leopard-ai/betty
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/lgcollins/tr-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/lif31up/model-agnostic-meta-learning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/lif31up/prototypical-network
https://github.com/llan-ml/tesp
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/mari-linhares/tensorflow-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/mariehane/prototypical-networks
https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch
https://github.com/mikehuisman/revisiting-learned-optimizers
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/mindspore-ai/models/tree/master/research/cv/ProtoNet
https://github.com/minseop-aitrics/FewshotLearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/msfuxian/DualAttentionNet
https://github.com/novicestone/meta-cetm
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/nyu-larx/attack_metarl
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch
https://github.com/oscarknagg/few-shot
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/potsawee/maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/prajjwal1/fluence
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/psh150204/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ray-project/ray/tree/master/rllib
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.