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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1981–1990 of 3569 papers

TitleStatusHype
Task Agnostic Meta-Learning for Few-Shot Learning—0
Task-Agnostic Semantic Communications Relying on Information Bottleneck and Federated Meta-Learning—0
Task Aligned Generative Meta-learning for Zero-shot Learning—0
Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation—0
Task Attended Meta-Learning for Few-Shot Learning—0
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware—0
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems—0
Task-Aware Part Mining Network for Few-Shot Learning—0
Task Calibration for Distributional Uncertainty in Few-Shot Classification—0
Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified