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 1076–1100 of 3569 papers

TitleStatusHype
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning—0
AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All—0
Generalizable Person Re-Identification by Domain-Invariant Mapping Network—0
A Unified Framework with Meta-dropout for Few-shot Learning—0
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization—0
AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Augmenting Supervised Learning by Meta-learning Unsupervised Local Rules—0
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
Generalizable speech deepfake detection via meta-learned LoRA—0
Defending against Poisoning Backdoor Attacks on Federated Meta-learning—0
Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization—0
Augmentation Learning for Semi-Supervised Classification—0
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition—0
All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)—0
3D Meta-Segmentation Neural Network—0
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online—0
Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning—0
Attribute Propagation Network for Graph Zero-shot Learning—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
All in One: Multi-task Prompting for Graph Neural Networks—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Generalizable Neural Fields as Partially Observed Neural Processes—0
Generalization in Neural Networks: A Broad Survey—0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—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