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 1131–1140 of 3569 papers

TitleStatusHype
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments—0
3D Meta-Registration: Meta-learning 3D Point Cloud Registration Functions—0
Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning—0
Deep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach—0
Attacking Few-Shot Classifiers with Adversarial Support Poisoning—0
Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds—0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Deep domain adaptation for polyphonic melody extraction—0
Attacking Few-Shot Classifiers with Adversarial Support Sets—0
A Thorough Review on Recent Deep Learning Methodologies for Image Captioning—0
Show:102550
← PrevPage 114 of 357Next →
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