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 3101–3125 of 3569 papers

TitleStatusHype
Invariant Meta Learning for Out-of-Distribution Generalization—0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning—0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks—0
Investigating Relative Performance of Transfer and Meta Learning—0
IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach—0
IPNET:Influential Prototypical Networks for Few Shot Learning—0
Is Fast Adaptation All You Need?—0
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?—0
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?—0
Is Pre-training Truly Better Than Meta-Learning?—0
Is Support Set Diversity Necessary for Meta-Learning?—0
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?—0
Investigating Active Learning and Meta-Learning for Iterative Peptide Design—0
Joint autoencoders: a flexible meta-learning framework—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification—0
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
Knowledge Consolidation based Class Incremental Online Learning with Limited Data—0
Knowledge-driven Meta-learning for CSI Feedback—0
Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils—0
Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning—0
Known Operator Learning and Hybrid Machine Learning in Medical Imaging --- A Review of the Past, the Present, and the Future—0
Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads—0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—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