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 3026–3050 of 3569 papers

TitleStatusHype
Meta Segmentation Network for Ultra-Resolution Medical Images—0
Curriculum in Gradient-Based Meta-Reinforcement Learning—0
Few-Shot Few-Shot Learning and the role of Spatial Attention—0
Theoretical Convergence of Multi-Step Model-Agnostic Meta-LearningCode0
Differentiable Bandit Exploration—0
Learning Adaptive Loss for Robust Learning with Noisy Labels—0
Task-Robust Model-Agnostic Meta-Learning—0
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement LearningCode0
Incremental Meta-Learning via Indirect Discriminant Alignment—0
On Parameter Tuning in Meta-learning for Computer Vision—0
Meta-Learning across Meta-Tasks for Few-Shot Learning—0
Machine Learning Approaches For Motor Learning: A Short Review—0
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem—0
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Towards explainable meta-learning—0
Compositional ADAM: An Adaptive Compositional Solver—0
Local Nonparametric Meta-LearningCode0
GradMix: Multi-source Transfer across Domains and Tasks—0
ML-misfit: Learning a robust misfit function for full-waveform inversion using machine learning—0
Analyzing Policy Distillation on Multi-Task Learning and Meta-Reinforcement Learning in Meta-World—0
Task Augmentation by Rotating for Meta-LearningCode0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Revisiting Meta-Learning as Supervised Learning—0
Extreme Algorithm Selection With Dyadic Feature RepresentationCode0
Optimized Generic Feature Learning for Few-shot Classification across Domains—0
Show:102550
← PrevPage 122 of 143Next →
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