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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/Duan-JM/awesome-papers-fewshot

Meta-Learning in Neural Networks: A Survey

https://github.com/DuoJiang/Meta-Continual-Learning-NLP

Meta-Learning Representations for Continual Learning

https://github.com/EdNiehues/simulacao-de-projeto-para-mapeamento

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/EightRice/atn_GPT-3

Language Models are Few-Shot Learners

https://github.com/ElementAI/TADAM

TADAM: Task dependent adaptive metric for improved few-shot learning

https://github.com/ElementAI/am3

Adaptive Cross-Modal Few-Shot Learning

https://github.com/EleutherAI/gpt-neo

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/EleutherAI/lm_evaluation_harness

Language Models are Few-Shot Learners

https://github.com/EmilyAlsentzer/clinicalBERT

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

https://github.com/Epi-NMT/DADG

Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation

https://github.com/ErickRosete/MAML-Imitation-Learning

One-Shot Visual Imitation Learning via Meta-Learning

https://github.com/Faceplugin-ltd/FaceRecognition-Android

Learning Meta Face Recognition in Unseen Domains

https://github.com/GMC-DRL/ConfigX

ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning

https://github.com/GMC-DRL/MetaBox

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

https://github.com/Gaopeng-Bai/MANN_model

One-shot Learning with Memory-Augmented Neural Networks

https://github.com/GenDisc/IVR

Evaluating Weakly Supervised Object Localization Methods Right

https://github.com/GeorgeDUT/MetaRLSAS

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/GinZhu/RDST

MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution

https://github.com/GuChenghs/MCRNet

Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach

https://github.com/Guang000/Awesome-Dataset-Distillation/blob/main/README.md

Distilled One-Shot Federated Learning

https://github.com/HAHA-DL/MLDG

Learning to Generalize: Meta-Learning for Domain Generalization

https://github.com/HLTCHKUST/UnifiedM2

On Unifying Misinformation Detection

https://github.com/HazeDT/WaveletKernelNet

Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

https://github.com/HenryCWoo/LTLProject

Prototypical Networks for Few-shot Learning

https://github.com/Hihippie/Learning2Capture

Free Lunch for Few-shot Learning: Distribution Calibration

https://github.com/HilbertXu/MAML-Tensorflow

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/HoganZhang/few-shot-gnn

Few-Shot Learning with Graph Neural Networks

https://github.com/Hsankesara/DeepResearch

Prototypical Networks for Few-shot Learning

https://github.com/IBM/BCM

https://github.com/IBM/cdfsl-benchmark

A Broader Study of Cross-Domain Few-Shot Learning

https://github.com/IBM/retrieve-write-slot-filling

Robust Retrieval Augmented Generation for Zero-shot Slot Filling

https://github.com/Ierezell/PapierFewShot

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/InsaneLife/ChineseNLPCorpus

FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding

https://github.com/IntelligentSystemsLab/ST-EVCDP

A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction

https://github.com/Ipsedo/TabPFN

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

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