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/matt-su/dr-adapter

공식 코드

Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation

https://github.com/matthewcym/sflm

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Revisiting Self-Training for Few-Shot Learning of Language Model

https://github.com/matthias-wright/art-fid

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ArtFID: Quantitative Evaluation of Neural Style Transfer

https://github.com/mattochal/enhanced-outlier-logits

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EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits

https://github.com/mattochal/imbalanced_fsl_public

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Few-Shot Learning with Class Imbalance

https://github.com/mattriemer/mer

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Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

https://github.com/maudzung/SFA3D

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Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment

https://github.com/maxlchen/Speech-PreFinetuning

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Pre-Finetuning for Few-Shot Emotional Speech Recognition

https://github.com/maxwellyaoni/pace

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PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization

https://github.com/maybenotime/css

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A New Dataset and Empirical Study for Sentence Simplification in Chinese

https://github.com/mayug/basetransformers

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BaseTransformers: Attention over base data-points for One Shot Learning

https://github.com/mbonto/fewshot_generalization

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Predicting the Accuracy of a Few-Shot Classifier

https://github.com/mbonto/fewshot_neuroimaging_classification

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Few-shot Decoding of Brain Activation Maps

https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation

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Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

https://github.com/mboudiaf/TIM

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Transductive Information Maximization For Few-Shot Learning

https://github.com/mboudiaf/pytorch-meta-dataset

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Mutual-Information Based Few-Shot Classification

https://github.com/mcd4874/neurips_competition

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2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets

https://github.com/mcg-nju/fsl-video

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A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark

https://github.com/mdausort/Cytology-fine-tuning

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Exploring Foundation Models Fine-Tuning for Cytology Classification

https://github.com/mdrpanwar/icl-bayesian-prism

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In-Context Learning through the Bayesian Prism

https://github.com/megvii-research/DPGN

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DPGN: Distribution Propagation Graph Network for Few-shot Learning

https://github.com/menelau/mlrs-pds

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MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelines

https://github.com/menglcool/segic

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SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation

https://github.com/mengmenm/SMIL

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SMIL: Multimodal Learning with Severely Missing Modality

https://github.com/mennafateen/modular-asas-f-rag

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Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback

https://github.com/menyifang/dct-net

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DCT-Net: Domain-Calibrated Translation for Portrait Stylization

https://github.com/merlresearch/SMART

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Are Deep Neural Networks SMARTer than Second Graders?

https://github.com/meryemmhamdi1/meta_cross_nlu_qa

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X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering

https://github.com/mesaysama/amharic-handwritten-character-dataset

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Offline Handwritten Amharic Character Recognition Using Few-shot Learning

https://github.com/mesophil/myriadal

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MyriadAL: Active Few Shot Learning for Histopathology

https://github.com/metabo-iclr2020/MetaBO

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Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

https://github.com/metaevo/awesome-metabbo

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

https://github.com/metaevo/metabox

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MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization

https://github.com/metamedseg/Code

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MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation

https://github.com/metavisionlab/metafbp

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MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction

https://github.com/mfranzs/meta-learning-curiosity-algorithms

공식 코드

Meta-learning curiosity algorithms

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