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 961970 of 3569 papers

TitleStatusHype
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot LearningCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Meta-Regularization by Enforcing Mutual-ExclusivenessCode0
Latent Task-Specific Graph Network SimulatorsCode0
Evolvability ES: Scalable and Direct Optimization of EvolvabilityCode0
Adaptive Domain-Specific Normalization for Generalizable Person Re-IdentificationCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
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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