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

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Showing 721–730 of 3569 papers

TitleStatusHype
Meta ControlNet: Enhancing Task Adaptation via Meta LearningCode1
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning—0
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness—0
Automating Continual LearningCode1
Interpretable Meta-Learning of Physical Systems—0
Scalable Meta-Learning with Gaussian Processes—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers—0
TIDE: Test Time Few Shot Object DetectionCode0
MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single Domain Generalization—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