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 2821–2830 of 3569 papers

TitleStatusHype
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification—0
MetaLoc: Learning to Learn Wireless Localization—0
Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization—0
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning—0
Meta Mask Correction for Nuclei Segmentation in Histopathological Image—0
MetaMIML: Meta Multi-Instance Multi-Label Learning—0
Meta Mirror Descent: Optimiser Learning for Fast Convergence—0
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization—0
MetaMix: Towards Corruption-Robust Continual Learning With Temporally Self-Adaptive Data Transformation—0
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning—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