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 1511–1520 of 3569 papers

TitleStatusHype
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation—0
Contextual Stochastic Bilevel Optimization—0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation—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
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All—0
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout—0
A Game-Theoretic Perspective of Generalization in Reinforcement 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