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 531–540 of 3569 papers

TitleStatusHype
Dynamic Relevance Learning for Few-Shot Object DetectionCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed RecognitionCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-LearningCode1
On the Convergence Theory for Hessian-Free Bilevel AlgorithmsCode1
Evolving Reinforcement Learning AlgorithmsCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
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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