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Meta-Learning

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하위 태스크: 3
평가표: 4
보존된 평가 기록: 13

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

<span style="color:grey; opacity: 0.6">( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )</span>

코드

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

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/ArmanAfrasiyabi/associative-alignment-fs

Associative Alignment for Few-shot Image Classification

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/AurelianTactics/dqnclipped_dqnreg_prelim_implementation

Evolving Reinforcement Learning Algorithms

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

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

https://github.com/BBDrive/TESP

Meta Reinforcement Learning with Task Embedding and Shared Policy

https://github.com/BeibinLi/SGIN

Sparsely Grouped Input Variables for Neural 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/BoLiu-SVCL/meta-open

Few-Shot Open-Set Recognition using Meta-Learning

https://github.com/CAVED123/METAWORLD

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

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/COMP6248-Reproducability-Challenge/COMP6248-Self-Supervised-Generalisation-with-Meta-Auxiliary-Learning

Self-Supervised Generalisation with Meta Auxiliary 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/ContinualAI/avalanche

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

https://github.com/DFrolova/ml-sk-project

Meta-learning framework with applications to zero-shot time-series forecasting

https://github.com/DMIU-ShELL/MOSAIC

Wasserstein Task Embedding for Measuring Task Similarities

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/DeepGraphLearning/FewShotRE

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

https://github.com/DeepStudio-TW/LSTM-optimizer

Learning to learn by gradient descent by gradient descent

https://github.com/DicksonWu654/Hybrid-Quantum-Classical-Neural-Networks-for-Diabetic-Retinopathy

Learning to learn with quantum neural networks via classical neural networks

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

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