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 2001–2025 of 3569 papers

TitleStatusHype
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection—0
Towards Explainable Meta-Learning for DDoS Detection—0
Context-aware Visual Tracking with Joint Meta-updating—0
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine LearningCode0
Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation: Correctly Choosing how to Choose Correctly—0
A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations—0
AutoProtoNet: Interpretability for Prototypical NetworksCode0
Diverse Preference Augmentation with Multiple Domains for Cold-start RecommendationsCode0
On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting—0
Scalable Semi-Modular Inference with Variational Meta-PosteriorsCode0
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines—0
Improved Relation Networks for End-to-End Speaker Verification and Identification—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations—0
Zero-shot meta-learning for small-scale data from human subjects—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
Exploring Frequency Adversarial Attacks for Face Forgery Detection—0
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
Sketch3T: Test-Time Training for Zero-Shot SBIR—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task DivisionCode0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Style-Guided Domain Adaptation for Face Presentation Attack Detection—0
Temporal Transductive Inference for Few-Shot Video Object SegmentationCode0
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison—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