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 601–610 of 3569 papers

TitleStatusHype
Context-Aware Meta-LearningCode1
Contrastive Meta Learning with Behavior Multiplicity for RecommendationCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Efficient Domain Generalization via Common-Specific Low-Rank DecompositionCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
Meta-Learning Dynamics Forecasting Using Task InferenceCode1
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-LearningCode1
Evading Forensic Classifiers with Attribute-Conditioned Adversarial FacesCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
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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