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 411–420 of 3569 papers

TitleStatusHype
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation ExploitationCode1
MetaHDR: Model-Agnostic Meta-Learning for HDR Image ReconstructionCode1
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic SegmentationCode1
SMIL: Multimodal Learning with Severely Missing ModalityCode1
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-IdentificationCode1
Adaptive-Control-Oriented Meta-Learning for Nonlinear SystemsCode1
Meta-Curriculum Learning for Domain Adaptation in Neural Machine TranslationCode1
Task-Adaptive Neural Network Search with Meta-Contrastive LearningCode1
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
Universal-Prototype Enhancing for Few-Shot Object DetectionCode1
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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