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 1171–1180 of 3569 papers

TitleStatusHype
A Knowledge-Driven Meta-Learning Method for CSI Feedback—0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
A Survey on Few-Shot Class-Incremental Learning—0
Guided Meta-Policy Search—0
DAWSON: A Domain Adaptive Few Shot Generation Framework—0
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?—0
A Survey of Reinforcement Learning for Optimization in Automation—0
Dataset Meta-Learning from Kernel Ridge-Regression—0
Dataset Meta-Learning from Kernel-Ridge Regression—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