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 3101–3110 of 3569 papers

TitleStatusHype
Invariant Meta Learning for Out-of-Distribution Generalization—0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning—0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks—0
Investigating Relative Performance of Transfer and Meta Learning—0
IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach—0
IPNET:Influential Prototypical Networks for Few Shot Learning—0
Is Fast Adaptation All You Need?—0
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?—0
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?—0
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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