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 151175 of 3569 papers

TitleStatusHype
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
Curriculum-Meta Learning for Order-Robust Continual Relation ExtractionCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Covariate Distribution Aware Meta-learningCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Are Deep Neural Networks SMARTer than Second Graders?Code1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
ArtFID: Quantitative Evaluation of Neural Style TransferCode1
AutoDebias: Learning to Debias for RecommendationCode1
Automating Continual LearningCode1
A Structured Dictionary Perspective on Implicit Neural RepresentationsCode1
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video RecognitionCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Diffusion-Based Neural Network Weights GenerationCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Attentional-Biased Stochastic Gradient DescentCode1
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object DetectionCode1
Discovering modular solutions that generalize compositionallyCode1
AirDet: Few-Shot Detection without Fine-tuning for Autonomous ExplorationCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
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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