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 2026–2050 of 3569 papers

TitleStatusHype
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation—0
The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing—0
Theoretical and Empirical Analysis of a Parallel Boosting Algorithm—0
Theoretical bounds on estimation error for meta-learning—0
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning—0
The Role of Global Labels in Few-Shot Classification and How to Infer Them—0
The Sample Complexity of Meta Sparse Regression—0
The Self-Learning Agent with a Progressive Neural Network Integrated Transformer—0
Thompson Sampling with Diffusion Generative Prior—0
Time Associated Meta Learning for Clinical Prediction—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
TinyMetaFed: Efficient Federated Meta-Learning for TinyML—0
TinyReptile: TinyML with Federated Meta-Learning—0
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions—0
TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning—0
Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks—0
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML—0
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies—0
Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling—0
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning—0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples—0
Toward Multimodal Model-Agnostic Meta-Learning—0
Towards 3D Semantic Scene Completion for Autonomous Driving: A Meta-Learning Framework Empowered by Deformable Large-Kernel Attention and Mamba Model—0
Towards an Unsupervised Method for Model Selection in Few-Shot 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