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 526550 of 3569 papers

TitleStatusHype
BOIL: Towards Representation Change for Few-shot LearningCode1
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed RecognitionCode1
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the WildCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Dynamic Relevance Learning for Few-Shot Object DetectionCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-LearningCode1
On the Convergence Theory for Hessian-Free Bilevel AlgorithmsCode1
Evolving Reinforcement Learning AlgorithmsCode1
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-LearningCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
Exploration in Approximate Hyper-State Space for Meta Reinforcement LearningCode1
Concept Learners for Few-Shot LearningCode1
Fast and Efficient Local Search for Genetic Programming Based Loss Function LearningCode1
Fast Context Adaptation via Meta-LearningCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
FewSAR: A Few-shot SAR Image Classification BenchmarkCode1
Few-shot Action Recognition with Prototype-centered Attentive LearningCode1
Few-Shot Class-Incremental Learning by Sampling Multi-Phase TasksCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
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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