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 3031–3040 of 3569 papers

TitleStatusHype
Learning Adaptive Loss for Robust Learning with Noisy Labels—0
Task-Robust Model-Agnostic Meta-Learning—0
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement LearningCode0
Incremental Meta-Learning via Indirect Discriminant Alignment—0
On Parameter Tuning in Meta-learning for Computer Vision—0
Meta-Learning across Meta-Tasks for Few-Shot Learning—0
Machine Learning Approaches For Motor Learning: A Short Review—0
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem—0
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Towards explainable meta-learning—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