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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/Gnomeek/Long-term-Cross-Adversarial-Training

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Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks

https://github.com/GoodAI/badger-2019

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BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

https://github.com/Gordonjo/versa

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Meta-Learning Probabilistic Inference For Prediction

https://github.com/Gorov/DiverseFewShot_Amazon

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Diverse Few-Shot Text Classification with Multiple Metrics

https://github.com/Guang000/Awesome-Dataset-Distillation

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A Comprehensive Survey of Dataset Distillation

https://github.com/GuangxingHan/QA-FewDet

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Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks

https://github.com/GuodongQi/FSL-OM

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Transductive Few-Shot Classification on the Oblique Manifold

https://github.com/HAIV-Lab/Knowe

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Coarse-To-Fine Incremental Few-Shot Learning

https://github.com/HKUSTCV/FSS-1000

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FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation

https://github.com/HLR/LvsR-LLM

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Learning vs Retrieval: The Role of In-Context Examples in Regression with LLMs

https://github.com/HLTCHKUST/PAML

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Personalizing Dialogue Agents via Meta-Learning

https://github.com/HMJiangGatech/harmless

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Meta Learning with Relational Information for Short Sequences

https://github.com/Haoqing-Wang/CDFSL-ATA

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Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

https://github.com/Haoqing-Wang/FSLSTM

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Few-shot Learning with LSSVM Base Learner and Transductive Modules

https://github.com/HardworkingPearl/EDG-DDA

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Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment

https://github.com/HayeonLee/MetaD2A

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Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets

https://github.com/HeatherJiaZG/SuperGlue-pytorch

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Physarum Powered Differentiable Linear Programming Layers and Applications

https://github.com/HeliosZhao/M3L

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Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification

https://github.com/Helsinki-NLP/Tatoeba-Challenge

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The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT

https://github.com/HenryPengZou/DeCrisisMB

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CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification

https://github.com/HeroResearchGroup/SmartSVM

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Fast Meta-Learning for Adaptive Hierarchical Classifier Design

https://github.com/Hippogriff/prifit

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PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation

https://github.com/HobbitLong/SupContrast

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Rethinking the Sample Relations for Few-Shot Classification

https://github.com/Homagn/Few_shot_clustering

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Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras

https://github.com/HosseinSimchi/META-IQA-CMRImages

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A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images

https://github.com/Howardqlz/Meta-MTL

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Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features

https://github.com/HumanCompatibleAI/population-irl

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Multi-task Maximum Entropy Inverse Reinforcement Learning

https://github.com/HzFu/COVID19_imaging_AI_paper_list

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Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

https://github.com/Hzfinfdu/MPMP

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Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning

https://github.com/IBM/nl2bash-eabench

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Execution-Based Evaluation of Natural Language to Bash and PowerShell for Incident Remediation

https://github.com/ICME-2025/HyperMAN

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HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation

https://github.com/ICTMCG/DITFEND

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

https://github.com/IIEdm/TaNP

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Task-adaptive Neural Process for User Cold-Start Recommendation

https://github.com/INK-USC/CLIF

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Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning

https://github.com/INK-USC/CrossFit

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CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP

https://github.com/IRVLUTD/FewSOL-DataLoader

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FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments

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