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 1031–1040 of 3569 papers

TitleStatusHype
Latent Task-Specific Graph Network SimulatorsCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
Learning advisor networks for noisy image classificationCode0
Cross-domain Transfer of Valence Preferences via a Meta-optimization ApproachCode0
Cross-domain Multi-modal Few-shot Object Detection via Rich TextCode0
A Hierarchical Bayesian Model for Deep Few-Shot Meta LearningCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Adaptive Cascading Network for Continual Test-Time AdaptationCode0
Feature Extractor Stacking for Cross-domain Few-shot 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