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 2551–2575 of 3569 papers

TitleStatusHype
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation—0
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Compositional ADAM: An Adaptive Compositional Solver—0
Compositional federated learning: Applications in distributionally robust averaging and meta learning—0
Compositional learning of functions in humans and machines—0
CoMPS: Continual Meta Policy Search—0
Concept Discovery for Fast Adapatation—0
Conditional Meta-Learning of Linear Representations—0
Conditional Mutual Information-Based Generalization Bound for Meta Learning—0
Conditional Super Learner—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
Confounder Identification-free Causal Visual Feature Learning—0
Confusable Learning for Large-class Few-Shot Classification—0
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning—0
Connection-Adaptive Meta-Learning—0
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification—0
Constrained Meta Agnostic Reinforcement Learning—0
Constructing a meta-learner for unsupervised anomaly detection—0
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments—0
Context-aware Visual Tracking with Joint Meta-updating—0
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—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