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/hl-hanlin/smkd

공식 코드

Supervised Masked Knowledge Distillation for Few-Shot Transformers

https://github.com/hlings/acrofod

공식 코드

AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection

https://github.com/hlr/metarevision

공식 코드

MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition

https://github.com/hmt2014/fewone

공식 코드

Classical Sequence Match is a Competitive Few-Shot One-Class Learner

https://github.com/hologerry/AGIS-Net

공식 코드

Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning

https://github.com/hongguangzhang/mlso-tmm-master

공식 코드

Multi-level Second-order Few-shot Learning

https://github.com/hongyujerrywang/featureextractorstacking

공식 코드

Feature Extractor Stacking for Cross-domain Few-shot Learning

https://github.com/horsepurve/deepvoro

공식 코드

Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach

https://github.com/hosodakazufumi/tfdone

공식 코드

It's DONE: Direct ONE-shot learning with quantile weight imprinting

https://github.com/hosseinshn/DGSML

공식 코드

Domain Generalization via Semi-supervised Meta Learning

https://github.com/hoyeoplee/MeLU

공식 코드

MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

https://github.com/hqucv/siamrn

공식 코드

Learning to Filter: Siamese Relation Network for Robust Tracking

https://github.com/hu-my/taskattributedistance

공식 코드

Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications

https://github.com/huang50213/AIM-Fewshot-Continual

공식 코드

Few-Shot and Continual Learning with Attentive Independent Mechanisms

https://github.com/huanzhang0320/document

공식 코드

Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning

https://github.com/huawei-noah/Pretrained-Language-Model

공식 코드

AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models

https://github.com/huaxiuyao/ARML

공식 코드

Automated Relational Meta-learning

https://github.com/huaxiuyao/ATS

공식 코드

Meta-learning with an Adaptive Task Scheduler

https://github.com/huaxiuyao/GFL

공식 코드

Graph Few-shot Learning via Knowledge Transfer

https://github.com/huaxiuyao/HSML

공식 코드

Hierarchically Structured Meta-learning

https://github.com/huaxiuyao/KGML

공식 코드

Knowledge-Aware Meta-learning for Low-Resource Text Classification

https://github.com/huaxiuyao/MetaMix

공식 코드

Improving Generalization in Meta-learning via Task Augmentation

https://github.com/huaxiuyao/MetaST

공식 코드

Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

https://github.com/huaxiuyao/mlti

공식 코드

Meta-Learning with Fewer Tasks through Task Interpolation

https://github.com/hubbs-lab-tamu/couple-stress-detection

공식 코드

A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships

https://github.com/huggingface/setfit

공식 코드

Efficient Few-Shot Learning Without Prompts

https://github.com/hugo-oliveira/fsws_metalearning

공식 코드

Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation

https://github.com/hugo101/platinum

공식 코드

PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information

https://github.com/hugofloresgarcia/music-trees

공식 코드

Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition

https://github.com/human-ai-research/hitl-few-shot-learning

공식 코드

Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning

https://github.com/humeai/competitions

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The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts

https://github.com/hurlab/vaccine-adjuvant-llm

공식 코드

Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language Models

https://github.com/hushell/pmf_cvpr22

공식 코드

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

https://github.com/hushell/sib_meta_learn

공식 코드

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

https://github.com/hust-linyi/cat-net

공식 코드

Few Shot Medical Image Segmentation with Cross Attention Transformer

https://github.com/hustvl/eva-x

공식 코드

EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning

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