Papers with Code 아카이브

Meta-Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/ThomasGoerttler/similarity-analysis-of-maml

공식 코드

Exploring the Similarity of Representations in Model-Agnostic Meta-Learning

https://github.com/Tianyi-Lei/Adaptive-Meta-learner-via-Gradient-Similarity-for-Few-shot-Text-Classification

공식 코드

Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification

https://github.com/ToFec/OneShotImageRegistration

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One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking

https://github.com/Tomiinek/Multilingual_Text_to_Speech

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One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech

https://github.com/TrinitialChan/DifFSS

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DifFSS: Diffusion Model for Few-Shot Semantic Segmentation

https://github.com/TristanThrush/few-shot-lm-learning

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Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization

https://github.com/Tsingularity/FRN

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Few-Shot Classification with Feature Map Reconstruction Networks

https://github.com/Tung-I/Dual-awareness-Attention-for-Few-shot-Object-Detection

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Dual-Awareness Attention for Few-Shot Object Detection

https://github.com/TungChintao/APM

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Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation

https://github.com/UKPLab/arxiv-2024-llm-trends

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Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science

https://github.com/UMass-Foundation-Model/VisualCoT

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See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning

https://github.com/VICO-UoE/URL

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Cross-domain Few-shot Learning with Task-specific Adapters

https://github.com/Vencent-Won/KRGTS-public

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Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction

https://github.com/WENGSYX/Self-Verification

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Large Language Models are Better Reasoners with Self-Verification

https://github.com/WOW5678/CoGraph

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Few-Shot Electronic Health Record Coding through Graph Contrastive Learning

https://github.com/WangJingyao07/MetaLearning-Lab

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AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywhere

https://github.com/WangYueFt/rfs

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Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?

https://github.com/WenboGong/MetaSGMCMC

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Meta-Learning for Stochastic Gradient MCMC

https://github.com/Wenlin-Chen/ADKF-IFT

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Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction

https://github.com/Wensi-Tang/DPSN

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Interpretable Time-series Classification on Few-shot Samples

https://github.com/WentaoChen0813/CDCS-FSL

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Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations

https://github.com/WentaoChen0813/SemanticPrompt

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Semantic Prompt for Few-Shot Image Recognition

https://github.com/WonderSeven/ULDA

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Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

https://github.com/WuYichen-97/Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-Corrector

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Learning to Purify Noisy Labels via Meta Soft Label Corrector

https://github.com/Wuziyi616/EventCLIP

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EventCLIP: Adapting CLIP for Event-based Object Recognition

https://github.com/XHomL/COCA

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COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation

https://github.com/Xiangyi1996/PPNet-PyTorch

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Part-aware Prototype Network for Few-shot Semantic Segmentation

https://github.com/XiaofengZhu/frosting_weights

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Frosting Weights for Better Continual Training

https://github.com/XingziXu/base_parabolic

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Base Models for Parabolic Partial Differential Equations

https://github.com/XuMengyaAmy/CIDACaptioning

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Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation

https://github.com/YDU-uva/VSM

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Learning to Learn Variational Semantic Memory

https://github.com/YDU-uva/VSM_WSD

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Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation

https://github.com/Yang-Bob/PMMs

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Prototype Mixture Models for Few-shot Semantic Segmentation

https://github.com/YangYongJin/APEX

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Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning

https://github.com/YannDubs/Neural-Process-Family

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Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

https://github.com/YazhouZhu19/RobustEMD

공식 코드

RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation

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