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 2761–2770 of 3569 papers

TitleStatusHype
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds—0
Online Structured Meta-learning—0
Conditional Mutual Information-Based Generalization Bound for Meta Learning—0
Meta-Learning Guarantees for Online Receding Horizon Learning Control—0
Meta-trained agents implement Bayes-optimal agents—0
3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence—0
Learning to Learn Variational Semantic MemoryCode0
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning—0
Meta-learning the Learning Trends Shared Across Tasks—0
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-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