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

Showing 34213430 of 3569 papers

TitleStatusHype
Meta-Learning Update Rules for Unsupervised Representation LearningCode0
Meta-learning using privileged information for dynamicsCode0
Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin EffectCode0
Sequential Skip Prediction with Few-shot in Streamed Music ContentsCode0
Frosting Weights for Better Continual TrainingCode0
Temporal Transductive Inference for Few-Shot Video Object SegmentationCode0
Towards Sample-efficient Overparameterized Meta-learningCode0
A Primal-Dual Subgradient Approachfor Fair Meta LearningCode0
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug DiscoveryCode0
FREE: Faster and Better Data-Free Meta-LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified