Papers with Code 아카이브

Few-Shot Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 12
평가표: 27
보존된 평가 기록: 52

Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.

Source: Penalty Method for Inversion-Free Deep Bilevel Optimization

코드

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

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bit

Big Transfer (BiT): General Visual Representation Learning

https://github.com/AUCOHL/RTL-Repo

GPT-4 Technical Report

https://github.com/AceChuse/DCN

Decoder Choice Network for Meta-Learning

https://github.com/AhmetSencan/MaskSplit-Self-supervised-Meta-learning-for-Few-shot-Semantic-Segmentation

MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation

https://github.com/AntheaLi/cs224nProject

Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining

https://github.com/AntreasAntoniou/DAGAN

Data Augmentation Generative Adversarial Networks

https://github.com/AntreasAntoniou/FewShotContinualLearningDataProvider

Defining Benchmarks for Continual Few-Shot Learning

https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch

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

https://github.com/AntreasAntoniou/Learning_to_Learn_via_Self-Critique

Learning to learn via Self-Critique

https://github.com/AntreasAntoniou/MatchingNetworks

Matching Networks for One Shot Learning

https://github.com/Arnavadi19/siamese-one-shot

https://github.com/ArnoutDevos/maml-cifar-fs

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

https://github.com/ArsenalCheng/Meta-Adapter

Meta-Adapter: An Online Few-shot Learner for Vision-Language Model

https://github.com/Atharva-Phatak/One-Shot-Art

Learning to Compare: Relation Network for Few-Shot Learning

https://github.com/BBDrive/Meta-SGD-RL

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

https://github.com/BilcSergiu/DAGAN

Data Augmentation Generative Adversarial Networks

https://github.com/Bingolby/medical_few_shot_segmentation

Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

https://github.com/CAVED123/omniglot-dataset

The Omniglot challenge: a 3-year progress report

https://github.com/CHeggan/Few-Shot-Classification-for-Audio-Evaluation

On sensitivity of meta-learning to support data

https://github.com/CLEANit/SRS2021

Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning

https://github.com/COAOX/Cifar-Prototypical

Prototypical Networks for Few-shot Learning

https://github.com/CSCYQJ/LOCATION-SENSITIVE-LOCAL-PROTOTYPE-NETWORK

'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images

https://github.com/CharlieDinh/FedU_FMTL

A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization

https://github.com/Chasing-After-AI/AI-papers

Few-shot Object Detection via Feature Reweighting

https://github.com/CocoJam/MAML

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

https://github.com/DanielShalam/SOT

Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions

https://github.com/DawoodChanti/IFSS-Net

IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound

https://github.com/Dipeshtamboli/Cross-Domain-FSL-via-NSAE

Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder

https://github.com/DrMMZ/ProtoNet

Prototypical Networks for Few-shot Learning

https://github.com/Duan-JM/awesome-papers-fewshot

Meta-Learning in Neural Networks: A Survey

https://github.com/EightRice/atn_GPT-3

Language Models are Few-Shot Learners

https://github.com/ElementAI/TADAM

TADAM: Task dependent adaptive metric for improved few-shot learning

https://github.com/ElementAI/am3

Adaptive Cross-Modal Few-Shot Learning

https://github.com/EleutherAI/gpt-neo

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm_evaluation_harness

Language Models are Few-Shot Learners

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