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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1376–1400 of 3569 papers

TitleStatusHype
Fast Adaptive Meta-Learning for Few-Shot Image GenerationCode0
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the TasksCode0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation BatteriesCode0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Iceberg: Enhancing HLS Modeling with Synthetic DataCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-LearningCode0
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
Meta-Learning Priors for Efficient Online Bayesian RegressionCode0
Persian Natural Language Inference: A Meta-learning approachCode0
Fast Adaptive Anomaly Detection—0
Fast-adapting and Privacy-preserving Federated Recommender System—0
Can Gradient Descent Simulate Prompting?—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
CAML: Fast Context Adaptation via Meta-Learning—0
Fast Adaptation for Human Pose Estimation via Meta-Optimization—0
Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification—0
Adaptive Variance Based Label Distribution Learning For Facial Age Estimation—0
FAM: fast adaptive federated meta-learning—0
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations—0
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation—0
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified