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 2581–2590 of 3569 papers

TitleStatusHype
Meta Federated Reinforcement Learning for Distributed Resource Allocation—0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning—0
MetaFollower: Adaptable Personalized Autonomous Car Following—0
Meta-Forecasting by combining Global Deep Representations with Local Adaptation—0
Meta-forests: Domain generalization on random forests with meta-learning—0
Meta-free few-shot learning via representation learning with weight averaging—0
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning—0
MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation—0
MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning—0
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—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