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 30763100 of 3569 papers

TitleStatusHype
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain SetupsCode0
Inverse Learning with Extremely Sparse Feedback for RecommendationCode0
Multidimensional Belief Quantification for Label-Efficient Meta-LearningCode0
Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot LearningCode0
When Does Self-supervision Improve Few-shot Learning?Code0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
Representation based meta-learning for few-shot spoken intent recognitionCode0
Style Interleaved Learning for Generalizable Person Re-identificationCode0
Meta Distribution Alignment for Generalizable Person Re-IdentificationCode0
Interpretable Meta-Measure for Model PerformanceCode0
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph GenerationCode0
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
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networksCode0
Reproducibility Report: La-MAML: Look-ahead Meta Learning for Continual LearningCode0
Reproducing Meta-learning with differentiable closed-form solversCode0
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot LearningCode0
Top-Related Meta-Learning Method for Few-Shot Object DetectionCode0
Style Variable and Irrelevant Learning for Generalizable Person Re-identificationCode0
Zero-shot task adaptation by homoiconic meta-mappingCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Multi-Modal Fusion by Meta-InitializationCode0
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh RecoveryCode0
In-Context Learning through the Bayesian PrismCode0
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
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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