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 571–580 of 3569 papers

TitleStatusHype
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
Direct Differentiable Augmentation SearchCode1
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
Concrete Subspace Learning based Interference Elimination for Multi-task Model FusionCode1
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social MediaCode1
Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-LearningCode1
Concept Learners for Few-Shot LearningCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
Curriculum-Meta Learning for Order-Robust Continual Relation ExtractionCode1
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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