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/destiny301/pmfl

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PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records

https://github.com/devavrattomar/sst

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Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

https://github.com/dfki-nlp/cross-ling-adr

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Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective

https://github.com/di0002ya/rfnet

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RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition

https://github.com/diegodoimo/geometry_icl_finetuning

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The representation landscape of few-shot learning and fine-tuning in large language models

https://github.com/digitalhealthcarelab/25multimodalfoundationmodel

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MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer Analysis

https://github.com/digitalphonetics/ims-toucan

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Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features

https://github.com/dimesteam/dimes

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DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems

https://github.com/dineshsrivasthav/adaptive_meta_learning_with_multi_agent_framework

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Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization

https://github.com/dingmyu/DAPN

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Domain-Adaptive Few-Shot Learning

https://github.com/divyam3897/MNG_AC

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Learning to Generate Noise for Multi-Attack Robustness

https://github.com/dmis-lab/biobert

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BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/doerlbh/UnsupervisedAttentionMechanism

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Unsupervised Attention Mechanism across Neural Network Layers

https://github.com/dojunpark/pragmatic_eval_korean

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Pragmatic Competence Evaluation of Large Language Models for the Korean Language

https://github.com/donghao51/moosa

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Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision

https://github.com/dongmanqing/Code-for-MAMO

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MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

https://github.com/dongsky/few-shot-vit

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Self-Promoted Supervision for Few-Shot Transformer

https://github.com/dousia/metaprompting

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MetaPrompting: Learning to Learn Better Prompts

https://github.com/dragondescentzerotsu/ssm-sam

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Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning

https://github.com/dstamos/Parameter-free-MTL

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Online Parameter-Free Learning of Multiple Low Variance Tasks

https://github.com/dual-grp/fedu_fmtl

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A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization

https://github.com/duanlab123/metaue

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MetaUE: Model-based Meta-learning for Underwater Image Enhancement

https://github.com/dubiouscactus/meta-learning-hope

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A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar?

https://github.com/ducminhkhoi/FAPIS

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FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter

https://github.com/ducminhkhoi/Feature-Weighting-and-Boosting

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Feature Weighting and Boosting for Few-Shot Segmentation

https://github.com/dunnolab/xland-minigrid

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XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

https://github.com/duongnhatthang/meta-bandit

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Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms

https://github.com/durgas16/AMAL

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Adaptive Mixing of Auxiliary Losses in Supervised Learning

https://github.com/dustincarrion/cd-metadl

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Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

https://github.com/dut-lujunyu/disagreement

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Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement

https://github.com/dvirsamuel/DRAGON

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From Generalized zero-shot learning to long-tail with class descriptors

https://github.com/dvlab-research/gfs-seg

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Generalized Few-shot Semantic Segmentation

https://github.com/dvornikita/SUR

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Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification

https://github.com/dvtailor/meta-l2d

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Learning to Defer to a Population: A Meta-Learning Approach

https://github.com/dyballa/generalization

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A separability-based approach to quantifying generalization: which layer is best?

https://github.com/dydjw9/MetaAttack_ICLR2020

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Query-efficient Meta Attack to Deep Neural Networks

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