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 3471–3480 of 3569 papers

TitleStatusHype
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror DescentCode0
Meta-Learning with Warped Gradient DescentCode0
Meta-learnt priors slow down catastrophic forgetting in neural networksCode0
Cross-domain Transfer of Valence Preferences via a Meta-optimization ApproachCode0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Approximately Equivariant Neural ProcessesCode0
Cross-domain Multi-modal Few-shot Object Detection via Rich TextCode0
Should We Be Pre-training? An Argument for End-task Aware Training as an AlternativeCode0
PICASO: Permutation-Invariant Cascaded Attentional Set OperatorCode0
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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