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 151200 of 3569 papers

TitleStatusHype
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person RetrievalCode1
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-LearningCode1
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the WildCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
A General Descent Aggregation Framework for Gradient-based Bi-level OptimizationCode1
Concrete Subspace Learning based Interference Elimination for Multi-task Model FusionCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series ModelingCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
Control-oriented meta-learningCode1
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
Cross-Market Product RecommendationCode1
CURI: A Benchmark for Productive Concept Learning Under UncertaintyCode1
A Channel Coding Benchmark for Meta-LearningCode1
Data Augmentation for Meta-LearningCode1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
AirDet: Few-Shot Detection without Fine-tuning for Autonomous ExplorationCode1
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object DetectionCode1
Procedural generation of meta-reinforcement learning tasksCode1
A Large Scale Search Dataset for Unbiased Learning to RankCode1
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
Bayesian Model-Agnostic Meta-LearningCode1
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural NetworksCode1
Discovering modular solutions that generalize compositionallyCode1
Discovering Reinforcement Learning AlgorithmsCode1
BOIL: Towards Representation Change for Few-shot LearningCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype EnhancementCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-LearningCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Amortized Probabilistic Conditioning for Optimization, Simulation and InferenceCode1
Efficient Domain Generalization via Common-Specific Low-Rank DecompositionCode1
Empirical Bayes Transductive Meta-Learning with Synthetic GradientsCode1
BaMBNet: A Blur-aware Multi-branch Network for Defocus DeblurringCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Evading Forensic Classifiers with Attribute-Conditioned Adversarial FacesCode1
Evolving Reinforcement Learning AlgorithmsCode1
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-LearningCode1
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
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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