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

Showing 11761200 of 3569 papers

TitleStatusHype
Improving Generalization of Meta-Learning With Inverted Regularization at Inner-Level0
Ground-Truth Free Meta-Learning for Deep Compressive Sampling0
An Erudite Fine-Grained Visual Classification Model0
Generalizable Black-Box Adversarial Attack with Meta LearningCode1
Eliminating Meta Optimization Through Self-Referential Meta Learning0
Wormhole MAML: Meta-Learning in Glued Parameter Space0
Learning to Detect Noisy Labels Using Model-Based FeaturesCode1
On Implicit Bias in Overparameterized Bilevel Optimization0
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer0
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of GeneralizationCode1
Robust Meta-Representation Learning via Global Label Inference and ClassificationCode0
Reusable Options through Gradient-based Meta LearningCode0
LogAnMeta: Log Anomaly Detection Using Meta Learning0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation0
Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality0
Asynchronous Distributed Bilevel OptimizationCode0
Are Deep Neural Networks SMARTer than Second Graders?Code1
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving0
One-shot skill assessment in high-stakes domains with limited data via meta learningCode0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs0
Transformers learn in-context by gradient descentCode2
MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized AdaptationCode2
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen EstimatorCode1
Instance-Conditional Timescales of Decay for Non-Stationary Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified