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 32313240 of 3569 papers

TitleStatusHype
Diverse Preference Augmentation with Multiple Domains for Cold-start RecommendationsCode0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine LearningCode0
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain ValidationCode0
Discovering Weight Initializers with Meta LearningCode0
Boosting Lightweight Single Image Super-resolution via Joint-distillationCode0
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Differentiable plasticity: training plastic neural networks with backpropagationCode0
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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