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 876–900 of 3569 papers

TitleStatusHype
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification—0
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification—0
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning—0
You Can Backdoor Personalized Federated LearningCode1
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation—0
Regularizing Neural Networks with Meta-Learning Generative Models—0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning—0
A behavioural transformer for effective collaboration between a robot and a non-stationary human—0
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty—0
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition—0
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning—0
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image SegmentationCode1
Nonlinear Meta-Learning Can Guarantee Faster Rates—0
Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors—0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Forecasting Early with Meta LearningCode0
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured LearningCode1
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—0
Towards Task Sampler Learning for Meta-LearningCode1
Exploiting Field Dependencies for Learning on Categorical DataCode0
A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access—0
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
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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