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 2226–2250 of 3569 papers

TitleStatusHype
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition—0
Across-Task Neural Architecture Search via Meta Learning—0
Actively Seeking and Learning from Live Data—0
Adaptable Text Matching via Meta-Weight Regulator—0
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations—0
Adaptation of Quadruped Robot Locomotion with Meta-Learning—0
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model—0
Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling—0
Adapting to the Unknown: Robust Meta-Learning for Zero-Shot Financial Time Series Forecasting—0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning—0
Adaptive Bayesian Linear Regression for Automated Machine Learning—0
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data—0
Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks—0
Adaptive Ensemble Learning with Confidence Bounds—0
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
Adaptive Gradient-Based Meta-Learning Methods—0
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection—0
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Adaptive Hierarchical Hyper-gradient Descent—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