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 1221–1230 of 3569 papers

TitleStatusHype
Contextualizing Meta-Learning via Learning to DecomposeCode0
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh RecoveryCode0
Improving Meta-Continual Learning Representations with Representation ReplayCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Few-shot Conformal Prediction with Auxiliary TasksCode0
MAD: Meta Adversarial Defense BenchmarkCode0
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain SchedulerCode0
Clustering Indices based Automatic Classification Model SelectionCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
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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