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 12261250 of 3569 papers

TitleStatusHype
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-LearningCode0
Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-LearningCode0
CMML: Contextual Modulation Meta Learning for Cold-Start RecommendationCode0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation ProcessingCode0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
Few-shot Conformal Prediction with Auxiliary TasksCode0
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain SchedulerCode0
Clustering Indices based Automatic Classification Model SelectionCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Interpretable Meta-Measure for Model PerformanceCode0
Continuous Meta-Learning without TasksCode0
Learning to Evolve on Dynamic GraphsCode0
Generalization Bounds For Meta-Learning: An Information-Theoretic AnalysisCode0
Improving Memory Efficiency for Training KANs via Meta LearningCode0
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative EmbeddingsCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
Improving Meta-Continual Learning Representations with Representation ReplayCode0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Clustered Task-Aware Meta-Learning by Learning from Learning PathsCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Few-shot classification in Named Entity Recognition TaskCode0
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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