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 451500 of 3569 papers

TitleStatusHype
Few-shot Relation Extraction via Bayesian Meta-learning on Relation GraphsCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
Few-shot Scene-adaptive Anomaly DetectionCode1
MIASSR: An Approach for Medical Image Arbitrary Scale Super-ResolutionCode1
Induction Networks for Few-Shot Text ClassificationCode1
Few-shot Text Classification with Distributional SignaturesCode1
Few-shot Visual Relationship Co-localizationCode1
Few-Shot Unsupervised Continual Learning through Meta-ExamplesCode1
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
Fine-grained Recognition with Learnable Semantic Data AugmentationCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
MotherNet: Fast Training and Inference via Hyper-Network TransformersCode1
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level TransferCode1
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Flexible Dataset Distillation: Learn Labels Instead of ImagesCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and DisorderCode1
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen EstimatorCode1
Multiple Meta-model Quantifying for Medical Visual Question AnsweringCode1
HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-LearningCode1
Harnessing Meta-Learning for Improving Full-Frame Video StabilizationCode1
Generalising via Meta-Examples for Continual Learning in the WildCode1
Neural Diffusion ProcessesCode1
BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series ModelingCode1
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in ContextCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive LearningCode1
Generalizable Implicit Neural Representations via Instance Pattern ComposersCode1
Generalizable No-Reference Image Quality Assessment via Deep Meta-learningCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learningCode1
Gradient-based Hyperparameter Optimization Over Long HorizonsCode1
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-LearningCode1
Nonrigid Reconstruction of Freehand Ultrasound without a TrackerCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
Amortized Probabilistic Conditioning for Optimization, Simulation and InferenceCode1
GenSDF: Two-Stage Learning of Generalizable Signed Distance FunctionsCode1
Adaptive Subspaces for Few-Shot LearningCode1
OmniPrint: A Configurable Printed Character SynthesizerCode1
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot LearningCode1
CD-FSOD: A Benchmark for Cross-domain Few-shot Object DetectionCode1
A contrastive rule for meta-learningCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
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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