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 2061–2070 of 3569 papers

TitleStatusHype
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation—0
Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning—0
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Towards intervention-centric causal reasoning in learning agents—0
Towards Learning to Remember in Meta Learning of Sequential Domains—0
Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning—0
Towards meta-learning for multi-target regression problems—0
Towards more efficient agricultural practices via transformer-based crop type classification—0
Towards Multi-Domain Single Image Dehazing via Test-Time Training—0
Towards Reliable Neural Machine Translation with Consistency-Aware 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