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 341–350 of 3569 papers

TitleStatusHype
From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication SystemsCode1
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IVCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Generalising via Meta-Examples for Continual Learning in the WildCode1
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain GeneralizationCode1
Generalizable Implicit Neural Representations via Instance Pattern ComposersCode1
Concept Learners for Few-Shot LearningCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
GenSDF: Two-Stage Learning of Generalizable Signed Distance FunctionsCode1
Context-Aware Meta-LearningCode1
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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