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One-Shot Learning

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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/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

Prototypical Networks for Few-shot Learning

https://github.com/llan-ml/tesp

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

https://github.com/ludwig-ai/ludwig

Ludwig: a type-based declarative deep learning toolbox

https://github.com/mari-linhares/tensorflow-maml

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

https://github.com/mariehane/prototypical-networks

Prototypical Networks for Few-shot Learning

https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch

Prototypical Networks for Few-shot Learning

https://github.com/michaelisc/cluttered-omniglot

One-Shot Instance Segmentation

https://github.com/microsoft/ptgnn

Typilus: Neural Type Hints

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

Prototypical Networks for Few-shot Learning

https://github.com/minseop-aitrics/FewshotLearning

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

https://github.com/ml4ai/mliis

One-Shot Learning for Semantic Segmentation

https://github.com/msfuxian/DualAttentionNet

Prototypical Networks for Few-shot Learning

https://github.com/nikola310/Siamese-Neural-Networks

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

Prototypical Networks for Few-shot Learning

https://github.com/oscarknagg/few-shot

Matching Networks for One Shot Learning

https://github.com/pavliclab/neurips2023-unireps-hong-tgsh-randomly_weighted_neuromodulation_in_neural_networks

For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions

https://github.com/philippe554/MANN

One-shot Learning with Memory-Augmented Neural 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/qitianwu/nodeformer

Matching Networks for One Shot Learning

https://github.com/raminux/python-control/tree/master/published_papers_repo/Automatica

Direct Data-Driven Discounted Infinite Horizon Linear Quadratic Regulator with Robustness Guarantees

https://github.com/ranpeng-git/few-shot-paddle

Matching Networks for One Shot Learning

https://github.com/ray-project/ray/tree/master/rllib

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

https://github.com/rdspring1/lsh_deeplearning

Learning to Remember Rare Events

https://github.com/rpeloff/multimodal_one_shot_learning

Multimodal One-Shot Learning of Speech and Images

https://github.com/s-a-malik/multi-few

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

https://github.com/sagieppel/MatSim-Dataset-Generator-Scripts-And-Neural-net

One-shot recognition of any material anywhere using contrastive learning with physics-based rendering

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.