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 841–850 of 3569 papers

TitleStatusHype
DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning—0
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference—0
AutoML for Contextual Bandits—0
A Meta Learning Approach to Discerning Causal Graph Structure—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
Domain Adaptation in Dialogue Systems using Transfer and Meta-Learning—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Fast Adaptive Anomaly Detection—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