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 981–990 of 3569 papers

TitleStatusHype
Memory Efficient Neural Processes via Constant Memory Attention BlockCode0
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic SegmentationCode1
Clustering Indices based Automatic Classification Model SelectionCode0
A Meta-learning based Generalizable Indoor Localization Model using Channel State Information—0
Improving Convergence and Generalization Using Parameter SymmetriesCode1
Effective Bilevel Optimization via Minimax Reformulation—0
Improved Compositional Generalization by Generating Demonstrations for Meta-Learning—0
Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-LearningCode0
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta LearningCode1
Single Domain Dynamic Generalization for Iris Presentation Attack Detection—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