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 576600 of 3569 papers

TitleStatusHype
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Grounded Language Learning Fast and SlowCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
Concept Learners for Few-Shot LearningCode1
Concrete Subspace Learning based Interference Elimination for Multi-task Model FusionCode1
A picture of the space of typical learnable tasksCode1
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive LearningCode1
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
How to train your MAMLCode1
How to Train Your MAML to Excel in Few-Shot ClassificationCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
Hyperparameter Importance Across DatasetsCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Improving Language Plasticity via Pretraining with Active ForgettingCode1
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IVCode1
Incremental Object Detection via Meta-LearningCode1
Influence-Balanced Loss for Imbalanced Visual ClassificationCode1
Instance Credibility Inference for Few-Shot LearningCode1
Interventional Few-Shot LearningCode1
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot RelationsCode1
Discovering modular solutions that generalize compositionallyCode1
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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