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 301325 of 3569 papers

TitleStatusHype
Few-Shot Scene Adaptive Crowd Counting Using Meta-LearningCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-LearningCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Exploiting Domain-Specific Features to Enhance Domain GeneralizationCode1
Few-shot Visual Relationship Co-localizationCode1
Are Deep Neural Networks SMARTer than Second Graders?Code1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
AReLU: Attention-based Rectified Linear UnitCode1
CD-FSOD: A Benchmark for Cross-domain Few-shot Object DetectionCode1
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed AdaptationCode1
Can Learned Optimization Make Reinforcement Learning Less Difficult?Code1
Exploration in Approximate Hyper-State Space for Meta Reinforcement LearningCode1
A General Descent Aggregation Framework for Gradient-based Bi-level OptimizationCode1
ArtFID: Quantitative Evaluation of Neural Style TransferCode1
Fuzzy Graph Neural Network for Few-Shot LearningCode1
Generalising via Meta-Examples for Continual Learning in the WildCode1
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data SetsCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Geometric Dataset Distances via Optimal TransportCode1
Evolving Reinforcement Learning AlgorithmsCode1
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEsCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Show:102550
← PrevPage 13 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