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 2601–2610 of 3569 papers

TitleStatusHype
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR NetworksCode0
Few-Shot Learning for Road Object Detection—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Similarity of Classification TasksCode0
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
Meta-learning on Spectral Images of Electroencephalogram of Schizophenics—0
Combat Data Shift in Few-shot Learning with Knowledge Graph—0
Meta-Learning for Effective Multi-task and Multilingual ModellingCode0
Meta-learning Based Beamforming Design for MISO Downlink—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