Deep learning models in medical image analysis often struggle with generalizability across domains and demographic groups due to data heterogeneity and scarcity. Traditional augmentation improves ...

Traditional machine learning algorithms for classification tasks operate under the assumption of balanced class distributions. However, this assumption only holds in some practical scenarios. In most ...

Machine learning models often perform impressively in the lab but struggle in the real world. The main culprit? Domain shift: the difference between the data a model was trained on and the data it ...

Department of Chemistry, Zhejiang University, Hangzhou 310058, Zhejiang Province, China Zhejiang Key Laboratory of Precise Synthesis of Functional Molecules, Department of Chemistry, School of Science ...

Traditional path planning algorithms often face problems such as local optimum traps and low monitoring efficiency in agricultural UAV operations, making it difficult to meet the operational ...

Modern malware programs employ sophisticated techniques to maintain persistent command and control (C2) communication with infected hosts while evading detection by security measures. Among these ...

Abstract: Domain generalization person re-identification (DG-ReID) aims to address the performance degradation caused by domain shift between the training domain and unseen target domains.

To solve the problem of poor generalization ability of the model on unknown data and the difference of physiological signals between different subjects. A sleep staging model based on Adversarial ...