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

Showing 28012810 of 3569 papers

TitleStatusHype
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification0
Explaining the Performance of Multi-label Classification Methods with Data Set Properties0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling0
Exploring Domain Shift in Extractive Text Summarization0
Exploring Frequency Adversarial Attacks for Face Forgery Detection0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study0
Exploring intra-task relations to improve meta-learning algorithms0
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified