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 1621–1630 of 3569 papers

TitleStatusHype
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Sketch3T: Test-Time Training for Zero-Shot SBIR—0
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task DivisionCode0
Style-Guided Domain Adaptation for Face Presentation Attack Detection—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Temporal Transductive Inference for Few-Shot Video Object SegmentationCode0
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison—0
Learn to Adapt for Monocular Depth Estimation—0
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification—0
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
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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