Papers with Code 아카이브

One-Shot Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 1
보존된 평가 기록: 1

One-shot learning is the task of learning information about object categories from a single training example.

<span style="color:grey; opacity: 0.6">( Image credit: Siamese Neural Networks for One-shot Image Recognition )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/sambi97/Low-shot-learning_-Deep-learning-project

Matching Networks for One Shot Learning

https://github.com/schatty/matching-networks-tf

Matching Networks for One Shot Learning

https://github.com/schatty/prototypical-networks-tf

Prototypical Networks for Few-shot Learning

https://github.com/schatty/siamese-networks-tf

The Omniglot challenge: a 3-year progress report

https://github.com/seblee97/task_weighted_maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/shaohua0116/MultiDigitMNIST

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/sheetalreddy/One-shot-learning

Active One-shot Learning

https://github.com/sheryl-ai/MetaPred

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/shoutOutYangJie/Few-Shot-Adversarial-Learning-for-face-swap

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/shunzh/pytorch-maml-rl

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/sicara/easy-few-shot-learning

Matching Networks for One Shot Learning

https://github.com/sidney1505/arc_maml_transformer

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/skingorz/prototypeNet-paddle

Prototypical Networks for Few-shot Learning

https://github.com/snowkylin/ntm

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/sonnguyentruong129/siamese-oneshot

https://github.com/sorenbouma/keras-oneshot

https://github.com/stefanowoerner/medimeta-pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/tensorfreitas/Siamese-Networks-for-One-Shot-Learning

https://github.com/theneuralbeing/maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/tianheyu927/mil

One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning

https://github.com/tristandeleu/ntm-one-shot

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/tristandeleu/pytorch-maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/tristandeleu/pytorch-maml-rl

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/ulises-jeremias/prototypical-networks-tf

Prototypical Networks for Few-shot Learning

https://github.com/vamsirk/FewShotLearning

One-Shot Learning for Semantic Segmentation

https://github.com/vamsirk/OneShotSemanticSegmentation

One-Shot Learning for Semantic Segmentation

https://github.com/vincent-thevenin/Realistic-Neural-Talking-Head-Models

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/vineetjain96/one-shot-mann

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/vivekatwal/projects

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/welkincici/MatchingNet

Matching Networks for One Shot Learning

https://github.com/wenbinlee/defensivefsl

Matching Networks for One Shot Learning

https://github.com/woaixuexixuexi/PSANet

One-Shot Learning for Semantic Segmentation

https://github.com/xcmax/FewShotWithoutForgetting

Dynamic Few-Shot Visual Learning without Forgetting

https://github.com/xiuyu0000/new_papers_codes/tree/main/MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/yangcedrus/ProtoNet

Prototypical Networks for Few-shot Learning

https://github.com/yaoyao-liu/mini-imagenet-tools

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.