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 1951–1975 of 3569 papers

TitleStatusHype
3D Meta-Registration: Meta-learning 3D Point Cloud Registration Functions—0
Learning to Adapt to Semantic Shift—0
Contrastive Learning is Just Meta-Learning—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding—0
Loss meta-learning for forecasting—0
Practical Conditional Neural Process Via Tractable Dependent Predictions—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
Loss Function Learning for Domain Generalization by Implicit Gradient—0
Revealing the Incentive to Cause Distributional Shift—0
Directional Domain Generalization—0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
Learning to Learn across Diverse Data Biases in Deep Face Recognition—0
Meta Learning with Minimax Regularization—0
Neural Variational Dropout Processes—0
Minimizing Memorization in Meta-learning: A Causal Perspective—0
Closed-form Sample Probing for Learning Generative Models in Zero-shot LearningCode0
Multi-Subspace Structured Meta-Learning—0
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation—0
Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—0
Dynamic Regret Analysis for Online Meta-Learning—0
Meta Learning on a Sequence of Imbalanced Domains with Difficulty AwarenessCode1
Generalization Bounds For Meta-Learning: An Information-Theoretic AnalysisCode0
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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