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 376400 of 3569 papers

TitleStatusHype
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and BeyondCode1
BaMBNet: A Blur-aware Multi-branch Network for Defocus DeblurringCode1
Meta-HAR: Federated Representation Learning for Human Activity RecognitionCode1
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and AdvertisingCode1
Towards mental time travel: a hierarchical memory for reinforcement learning agentsCode1
ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learningCode1
MIASSR: An Approach for Medical Image Arbitrary Scale Super-ResolutionCode1
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point DetectionCode1
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural NetworksCode1
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate PredictionCode1
Multiple Meta-model Quantifying for Medical Visual Question AnsweringCode1
AutoDebias: Learning to Debias for RecommendationCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph CompletionCode1
X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question AnsweringCode1
Editing Factual Knowledge in Language ModelsCode1
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual LearningCode1
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature AlignmentCode1
Generalizable No-Reference Image Quality Assessment via Deep Meta-learningCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
Direct Differentiable Augmentation SearchCode1
Show:102550
← PrevPage 16 of 143Next →

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