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/jwhitehill/compositionalclustering

공식 코드

Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification

https://github.com/jwu4sml/l2e

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A Unified Meta-Learning Framework for Dynamic Transfer Learning

https://github.com/jz-fun/meta_control_variates

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Meta-learning Control Variates: Variance Reduction with Limited Data

https://github.com/jzhang38/lpd

공식 코드

Better Few-Shot Relation Extraction with Label Prompt Dropout

https://github.com/kabouzeid/point2vec

공식 코드

point2vec for Self-Supervised Representation Learning on Point Clouds

https://github.com/kacperkan/conerf

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CoNeRF: Controllable Neural Radiance Fields

https://github.com/kahnchana/opl

공식 코드

Orthogonal Projection Loss

https://github.com/kaipengm2/DiffKendall

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DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation

https://github.com/kaist-dmlab/MeLON

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Meta-Learning for Online Update of Recommender Systems

https://github.com/kaist-lklab/cot-collection

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The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kaist-silab/devformer

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DevFormer: A Symmetric Transformer for Context-Aware Device Placement

https://github.com/kaist-silab/dppbench

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DevFormer: A Symmetric Transformer for Context-Aware Device Placement

https://github.com/kaist-silab/meta-sage

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Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization

https://github.com/kaistai/cot-collection

공식 코드

The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kaixin96/PANet

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PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

https://github.com/kaize0409/GPN

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Graph Prototypical Networks for Few-shot Learning on Attributed Networks

https://github.com/kaize0409/Meta-GDN_AnomalyDetection

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Few-shot Network Anomaly Detection via Cross-network Meta-learning

https://github.com/kaize0409/meta-pn

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Meta Propagation Networks for Graph Few-shot Semi-supervised Learning

https://github.com/kakaobrain/ginr-ipc

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Generalizable Implicit Neural Representations via Instance Pattern Composers

https://github.com/kalrfou/swint-pretrained-microscopy-models

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Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN Encoders

https://github.com/kargaranamir/MenuCraft

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MenuCraft: Interactive Menu System Design with Large Language Models

https://github.com/karim-53/pdll

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Pairwise Difference Learning for Classification

https://github.com/karpurapus/bddgpt-automate-tests

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Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior-Driven Development Acceptance Test Formulation

https://github.com/karthik-d/few-shot-learning-skin-analysis

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Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative Embeddings

https://github.com/kartikgill/easter2

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Easter2.0: Improving convolutional models for handwritten text recognition

https://github.com/katafeya/samovar

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Meta-Learning with Shared Amortized Variational Inference

https://github.com/katbailey/few-shot-text-classification

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Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop

https://github.com/kate-sann5100/SS_few_shot

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Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes

https://github.com/kate-sann5100/crossinstitutionfewshotsegmentation

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Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

https://github.com/katsumiok/askit-artifact

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AskIt: Unified Programming Interface for Programming with Large Language Models

https://github.com/katsumiok/pyaskit

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AskIt: Unified Programming Interface for Programming with Large Language Models

https://github.com/katsumiok/ts-askit

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AskIt: Unified Programming Interface for Programming with Large Language Models

https://github.com/kaushal0494/meta_xnlg

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Meta-X$_{NLG}$: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation

https://github.com/kavehhassani/metagrl

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Cross-Domain Few-Shot Graph Classification

https://github.com/kayuksel/robust-qd-portfolio-ensemble

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Generative Meta-Learning Robust Quality-Diversity Portfolio

https://github.com/kclip/bayesian_active_meta_learning

공식 코드

Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization

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