SOTAVerified

Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–210 of 3569 papers

TitleStatusHype
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Few-shot Decoding of Brain Activation MapsCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Few-Shot Learning with Class ImbalanceCode1
CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person RetrievalCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
Few-shot Object Detection via Feature ReweightingCode1
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed RecognitionCode1
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified