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/forgi86/sysid-transformers-transfer

공식 코드

On the adaptation of in-context learners for system identification

https://github.com/francescocappio/oshot-meta-learning

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Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection

https://github.com/frankzheng2022/prise

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PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control

https://github.com/frkl/modular-adaptation

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Modular Adaptation for Cross-Domain Few-Shot Learning

https://github.com/fsod/cd-fsod

공식 코드

CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection

https://github.com/fukexue/fast

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification

https://github.com/fukexue/mcp-bert

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Boosting Point-BERT by Multi-choice Tokens

https://github.com/gabriel-skantze/CoLLIE

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CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings

https://github.com/ganatra-v/gaussian-sampling-fsl

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Logarithm-transform aided Gaussian Sampling for Few-Shot Learning

https://github.com/gaoren002/Anomaly-multiclassification

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Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task

https://github.com/gaoxiangluo/llm-biomed-ner-er

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An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing

https://github.com/gasiki/eeg-reptile

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EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIs

https://github.com/gbrltv/process_meta_learning

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Using Meta-learning to Recommend Process Discovery Methods

https://github.com/gchochla/z2fsl

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End-to-end Generative Zero-shot Learning via Few-shot Learning

https://github.com/gdahia/meta_occ

공식 코드

Meta Learning for Few-Shot One-class Classification

https://github.com/genie-kim/pinthememory

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Pin the Memory: Learning to Generalize Semantic Segmentation

https://github.com/georgevern/lmcor

공식 코드

Small Language Models Improve Giants by Rewriting Their Outputs

https://github.com/georgiarg/autofashion

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Automatic Generation of Fashion Images using Prompting in Generative Machine Learning Models

https://github.com/georgosgeorgos/hierarchical-few-shot-generative-models

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SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation

https://github.com/ggjy/vision_weak_to_strong

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Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

https://github.com/gguibon/protoseq

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Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks

https://github.com/ghazalmilani/one-shot-learning-from-text-iclp2023

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Towards One-Shot Learning for Text Classification using Inductive Logic Programming

https://github.com/ghliu/mean-field-fcdnn

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Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective

https://github.com/gianlucarloni/causality_conv_nets

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Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results

https://github.com/giannifranchi/NAO_morpho

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Learning Deep Morphological Networks with Neural Architecture Search

https://github.com/gidariss/FewShotWithoutForgetting

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Dynamic Few-Shot Visual Learning without Forgetting

https://github.com/gidariss/wDAE_GNN_FewShot

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Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning

https://github.com/giilde/ft-vs-icl

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In-context Learning and Gradient Descent Revisited

https://github.com/gingasan/mometas

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Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning

https://github.com/gingl/cmpa

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Deeply Coupled Cross-Modal Prompt Learning

https://github.com/gitgyun/chameleon

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Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild

https://github.com/gitgyun/visual_token_matching

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Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching

https://github.com/githubnext/testpilot

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An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

https://github.com/gjiaying/keaf

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Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction

https://github.com/gjiaying/zslrc

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Zero-shot Relation Classification from Side Information

https://github.com/gmc-drl/awesome-metabbo

공식 코드

Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization

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