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 3051–3075 of 3569 papers

TitleStatusHype
Improving both domain robustness and domain adaptability in machine translation—0
Improving End-to-End Speech-to-Intent Classification with Reptile—0
Enhancing Few-Shot Image Classification with Unlabelled Examples—0
Improving Few-Shot Visual Classification with Unlabelled Examples—0
Improving Generalization of Meta-Learning With Inverted Regularization at Inner-Level—0
Improving Generalization via Meta-Learning on Hard Samples—0
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation—0
Improving the Generalization of Meta-learning on Unseen Domains via Adversarial Shift—0
Improving the performance of weak supervision searches using transfer and meta-learning—0
Improving the Reliability for Confidence Estimation—0
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning—0
Imputation of missing values in multi-view data—0
In-Context In-Context Learning with Transformer Neural Processes—0
In-Context Learning for Few-Shot Molecular Property Prediction—0
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory—0
In-context learning of evolving data streams with tabular foundational models—0
In-Context Meta LoRA Generation—0
Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment—0
Incremental Few-Shot Object Detection—0
Incremental Meta-Learning via Indirect Discriminant Alignment—0
Indic Languages Automatic Speech Recognition using Meta-Learning Approach—0
Inductive Linear Probing for Few-shot Node Classification—0
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning—0
Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning—0
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study—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