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 1976–2000 of 3569 papers

TitleStatusHype
TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition—0
Task-Adaptive Clustering for Semi-Supervised Few-Shot Classification—0
Task-Adaptive Pseudo Labeling for Transductive Meta-Learning—0
Task Agnostic Continual Learning via Meta Learning—0
Task-Agnostic Meta-Learning for Few-shot Learning—0
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
Task-Distributionally Robust Data-Free Meta-Learning—0
Task-driven Image Fusion with Learnable Fusion Loss—0
Task Fingerprinting for Meta Learning in Biomedical Image Analysis—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Task Relatedness-Based Generalization Bounds for Meta Learning—0
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression—0
Task-Specific Gradient Adaptation for Few-Shot One-Class Classification—0
Task Weighting in Meta-learning with Trajectory Optimisation—0
TalaGen: A System for Automatic Tala Identification and Generation—0
Teaching Models to Improve on Tape—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