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 2626–2650 of 3569 papers

TitleStatusHype
Dataset Meta-Learning from Kernel-Ridge Regression—0
Dataset Meta-Learning from Kernel Ridge-Regression—0
DAWSON: A Domain Adaptive Few Shot Generation Framework—0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity—0
Decoupled DETR For Few-shot Object Detection—0
Decoupled Pronunciation and Prosody Modeling in Meta-Learning-Based Multilingual Speech Synthesis—0
Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning—0
When MAML Can Adapt Fast and How to Assist When It Cannot—0
Deep domain adaptation for polyphonic melody extraction—0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds—0
Deep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach—0
Deep Learning with Label Noise: A Hierarchical Approach—0
Meta-Learning Mean Functions for Gaussian Processes—0
Deep Meta Learning for Real-Time Target-Aware Visual Tracking—0
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier—0
Deep Meta-learning in Recommendation Systems: A Survey—0
Deep Meta-Learning: Learning to Learn in the Concept Space—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
Deep Metric Learning via Adaptive Learnable Assessment—0
Deep neural network ensemble by data augmentation and bagging for skin lesion classification—0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification—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