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 1751–1760 of 3569 papers

TitleStatusHype
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity—0
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders—0
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks—0
Deep domain adaptation for polyphonic melody extraction—0
MetaASSIST: Robust Dialogue State Tracking with Meta LearningCode0
Low-Resource Multilingual and Zero-Shot Multispeaker TTS—0
Boosting Natural Language Generation from Instructions with Meta-Learning—0
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-LearningCode0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—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