Deep learning has been the answer to many machine learning problems during the past two
decades. However, it comes with two significant constraints: dependency on extensive …

In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …

Aiming to address the currently low accuracy of domestic industrial defect detection, this
paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved …

In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …

Numerous researches have been conducted on developing effective intelligent fault
diagnosis systems. As a commonly used deep learning technique, stacked autoencoders …

Age estimation has broad application prospects of many fields, such as video surveillance,
social networking, and human-computer interaction. However, many of the published age …

Abstract Domain generalization aims at training machine learning models to perform
robustly across different and unseen domains. Several methods train models from multiple …

Cataract is one of the most common eye disorders that causes vision distortion. Accurate
and timely detection of cataracts is the best way to control the risk and avoid blindness …

Due to the real working conditions, the collected mechanical fault datasets are actually
limited and always highly imbalanced, which restricts the diagnosis accuracy and stability …

Convolutional neural networks (CNNs) find vast applications in the field of image
processing. This study utilises the CNNs in conjunction with the crystal plasticity finite …