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 101150 of 3569 papers

TitleStatusHype
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object DetectionCode1
A Large Scale Search Dataset for Unbiased Learning to RankCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
Context-Aware Meta-LearningCode1
Adaptive-Control-Oriented Meta-Learning for Nonlinear SystemsCode1
Adaptive Multi-Teacher Knowledge Distillation with Meta-LearningCode1
Discovering modular solutions that generalize compositionallyCode1
Discovering Temporally-Aware Reinforcement Learning AlgorithmsCode1
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social MediaCode1
A Channel Coding Benchmark for Meta-LearningCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend ForecastingCode1
Concrete Subspace Learning based Interference Elimination for Multi-task Model FusionCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and DisorderCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Dynamic Relevance Learning for Few-Shot Object DetectionCode1
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-LearningCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Adaptive Subspaces for Few-Shot LearningCode1
A contrastive rule for meta-learningCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
Evading Forensic Classifiers with Attribute-Conditioned Adversarial FacesCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the WildCode1
CD-FSOD: A Benchmark for Cross-domain Few-shot Object DetectionCode1
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-LearningCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
Concept Learners for Few-Shot LearningCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series ModelingCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Bayesian Model-Agnostic Meta-LearningCode1
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
BaMBNet: A Blur-aware Multi-branch Network for Defocus DeblurringCode1
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Automating Outlier Detection via Meta-LearningCode1
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel LearningCode1
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural NetworksCode1
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot RelationsCode1
Automated Relational Meta-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