Papers with Code 아카이브

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/RongKaiWeskerMA/INSTA

공식 코드

Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning

https://github.com/RuohanW/Tasml

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Structured Prediction for Conditional Meta-Learning

https://github.com/RussianNLP/TAPE

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TAPE: Assessing Few-shot Russian Language Understanding

https://github.com/SLAMPAI/large-scale-pretraining-transfer

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Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images

https://github.com/SamyAteia/BioRAGent

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BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A

https://github.com/Sara-Ahmed/SiT

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SiT: Self-supervised vIsion Transformer

https://github.com/SawanKumar28/pero

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Reordering Examples Helps during Priming-based Few-Shot Learning

https://github.com/Seokju-Cho/Volumetric-Aggregation-Transformer

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Cost Aggregation Is All You Need for Few-Shot Segmentation

https://github.com/Sephiroth66616/MKDPINN

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Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life Prediction

https://github.com/Sha-Lab/CASTLE

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Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning

https://github.com/Sha-Lab/FEAT

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

https://github.com/Shiyan-cps/PointKAN-pytorch

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KAN or MLP? Point Cloud Shows the Way Forward

https://github.com/ShuaiLYU/MVREC

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MVREC: A General Few-shot Defect Classification Model Using Multi-View Region-Context

https://github.com/ShunchiZhang/ToM-in-AMC

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Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind

https://github.com/ShuoYang-1998/Few_Shot_Distribution_Calibration

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Free Lunch for Few-shot Learning: Distribution Calibration

https://github.com/ShuoYang-1998/ICLR2021-Oral_Distribution_Calibration

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Free Lunch for Few-shot Learning: Distribution Calibration

https://github.com/SkBlaz/tax2vec

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tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification

https://github.com/StanfordASL/ALPaCA

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Meta-Learning Priors for Efficient Online Bayesian Regression

https://github.com/StanfordASL/Adaptive-Control-Oriented-Meta-Learning

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Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems

https://github.com/StanfordASL/moca

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Continuous Meta-Learning without Tasks

https://github.com/StanfordASL/rover-meta-learning

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Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics

https://github.com/SteveImmanuel/OEM-Few-Shot-Learnable-Prompt

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Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain

https://github.com/StoneT2000/trajectorytranslation

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Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization

https://github.com/SunJiamei/few-shot-lrp-guided

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Explanation-Guided Training for Cross-Domain Few-Shot Classification

https://github.com/SungFeng-Huang/Meta-TTS

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Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech

https://github.com/SunjHan/Hybrid-Representation-Learning-Approach-for-Rare-Disease-Classification

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Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification

https://github.com/SuperSecretBioTech/evolutionary_monte_carlo_search

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Improving few-shot learning-based protein engineering with evolutionary sampling

https://github.com/Sycamoretail/PAM

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Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning

https://github.com/THU-BPM/MetaSRE

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Semi-supervised Relation Extraction via Incremental Meta Self-Training

https://github.com/THU-KEG/MetaKGR

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Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations

https://github.com/THUDM/ScenarioMeta

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Sequential Scenario-Specific Meta Learner for Online Recommendation

https://github.com/TJU-YDragonW/TAMT

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TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition

https://github.com/Tagore-7/Small-Language-Models-for-the-Democratization-of-Financial-Literacy

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Exploring the Readiness of Prominent Small Language Models for the Democratization of Financial Literacy

https://github.com/TalSchuster/CrossLingualELMo

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Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing

https://github.com/TalwalkarLab/leaf

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LEAF: A Benchmark for Federated Settings

https://github.com/TencentAILabHealthcare/Few-shot-WSI

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Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning

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