Although deep learning has achieved satisfactory performance in computer vision, a large
volume of images is required. However, collecting images is often expensive and …

Data augmentation (DA) is indispensable in modern machine learning and deep neural
networks. The basic idea of DA is to construct new training data to improve the model's …

Despite substantial progress in the field of deep learning, overfitting persists as a critical
challenge, and data augmentation has emerged as a particularly promising approach due to …

In spite of the great success of deep neural networks for many challenging classification
tasks, the learned networks are vulnerable to overfitting and adversarial attacks. Recently …

Data mixing augmentation have proved to be effective for improving the generalization
ability of deep neural networks. While early methods mix samples by hand-crafted policies …

Recent strategies achieved ensembling"" for free"" by fitting concurrently diverse
subnetworks inside a single base network. The main idea during training is that each …

Mixup augmentation has emerged as a widely used technique for improving the
generalization ability of deep neural networks (DNNs). However, the lack of standardized …

Mixup data augmentation approaches have been applied for various tasks of deep learning
to improve the generalization ability of deep neural networks. Some existing approaches …

To reduce the extreme label dependence of supervised product quantization methods, the
semi-supervised paradigm usually employs massive unlabeled data to assist in regularizing …

Defect inspection is an essential part of ensuring the quality of industrial products. Deep
learning has achieved great success in defect inspection when a large number of labeled …