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 2491–2500 of 3569 papers

TitleStatusHype
Adaptive Adversarial Training for Meta Reinforcement Learning—0
Learning to Learn to be Right for the Right Reasons—0
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning—0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification—0
Stateless Neural Meta-Learning using Second-Order GradientsCode0
Meta-learning for skin cancer detection using Deep Learning Techniques—0
A Meta-Learning Approach for Medical Image Registration—0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
On the Influence of Masking Policies in Intermediate Pre-training—0
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningCode0
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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