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 3001–3025 of 3569 papers

TitleStatusHype
Task-Adaptive Clustering for Semi-Supervised Few-Shot Classification—0
Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis—0
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of componentsCode0
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation—0
Incremental Few-Shot Object Detection—0
Learning State-Dependent Losses for Inverse Dynamics Learning—0
Single-View 3D Object Reconstruction from Shape Priors in Memory—0
Finding online neural update rules by learning to remember—0
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images—0
PAC-Bayes meta-learning with implicit task-specific posteriors—0
Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error—0
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning—0
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning—0
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?—0
Using a thousand optimization tasks to learn hyperparameter search strategies—0
Adversarial Monte Carlo Meta-Learning of Optimal Prediction ProceduresCode0
Provable Meta-Learning of Linear RepresentationsCode0
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning—0
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification—0
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning—0
The Sample Complexity of Meta Sparse Regression—0
Few-shot acoustic event detection via meta-learning—0
Meta-learning for mixed linear regression—0
Structured Prediction for Conditional Meta-LearningCode0
Personalized Federated Learning: A Meta-Learning Approach—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