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 25012510 of 3569 papers

TitleStatusHype
Boosting Model Resilience via Implicit Adversarial Data Augmentation0
Boosting Natural Language Generation from Instructions with Meta-Learning0
BP-DeepONet: A new method for cuffless blood pressure estimation using the physcis-informed DeepONet0
Brain-inspired global-local learning incorporated with neuromorphic computing0
Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection0
Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization0
Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study0
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning0
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning0
Budget-aware Few-shot Learning via Graph Convolutional Network0
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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