Preserving maximal information is the basic principle of designing self-supervised learning
methodologies. To reach this goal, contrastive learning adopts an implicit way which is …

Abstract Technologies such as Artificial Intelligence, Machine Learning, and Internet of
Things has made unobtrusive mental health monitoring a reality. Since, obtaining a large …

As an emerging early diagnostic technology for gastrointestinal diseases, confocal laser
endomicroscopy lacks large-scale perfect annotated data, leading to a major challenge in …

Introduction Computer vision and deep learning (DL) techniques have succeeded in a wide
range of diverse fields. Recently, these techniques have been successfully deployed in plant …

Current pre-training methods in computer vision focus on natural images in the daily-life
context. However, abstract diagrams such as icons and symbols are common and important …

The deep learning technique plays an important role in Wi-Fi localization systems as it could
mine deep features of measurement data. The main challenges are to combat the signal …

This paper presents an investigation of the challenges of training Deep Neural Networks
(DNNs) via self-supervised objectives, using local learning as a parallelizable alternative to …

Given its low dose and compactness, chest radiography has been widely used as the first-
line test to determine the presence of lung anomalies. Nevertheless, a high-performance …

机器学习在WiFi 指纹定位技术中扮演着重要角色. 针对信号波动对指纹辨识力的影响往往被
忽略以及如何从样本中提取更广泛的表征信息的问题, 提出了一种基于改进对比学习(CL) …

A semi-supervised convolutional neural network segmentation method of medical images
based on contrastive learning is proposed. The cardiac magnetic resonance imaging (MRI) …