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 1101–1125 of 3569 papers

TitleStatusHype
When Does MAML Objective Have Benign Landscape?—0
Gradient-based Meta-solving and Its Applications to Iterative Methods for Solving Differential Equations—0
Deep Reinforcement Learning for Traveling Purchaser Problems—0
Deep Reinforcement Learning, a textbook—0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification—0
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification—0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection—0
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
Deep neural network ensemble by data augmentation and bagging for skin lesion classification—0
Attentive Feature Reuse for Multi Task Meta learning—0
Algorithm Selection Framework for Cyber Attack Detection—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
Deep Metric Learning via Adaptive Learnable Assessment—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
Deep Meta-Learning: Learning to Learn in the Concept Space—0
Deep Meta-learning in Recommendation Systems: A Survey—0
Attention-based Few-Shot Person Re-identification Using Meta Learning—0
Algorithm Design for Online Meta-Learning with Task Boundary Detection—0
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier—0
Deep Meta Learning for Real-Time Target-Aware Visual Tracking—0
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation—0
Meta-Learning Mean Functions for Gaussian Processes—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