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 1391–1400 of 3569 papers

TitleStatusHype
Generalizable speech deepfake detection via meta-learned LoRA—0
A General framework for PAC-Bayes Bounds for Meta-Learning—0
Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling—0
Improved Compositional Generalization by Generating Demonstrations for Meta-Learning—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Improved Few-Shot Visual Classification—0
Improved Generalization Risk Bounds for Meta-Learning with PAC-Bayes-kl Analysis—0
Improved Meta Learning for Low Resource Speech Recognition—0
Improved Meta-Learning Training for Speaker Verification—0
Knowledge-driven Meta-learning for CSI Feedback—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