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 1431–1440 of 3569 papers

TitleStatusHype
ArtFID: Quantitative Evaluation of Neural Style TransferCode1
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
Can we achieve robustness from data alone?—0
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation—0
Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration—0
MetaComp: Learning to Adapt for Online Depth Completion—0
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
Tackling Long-Tailed Category Distribution Under Domain ShiftsCode1
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Test-Time Adaptation via Conjugate Pseudo-labelsCode1
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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