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

Synthesizing photo-realistic images based on text descriptions is a challenging task in the
field of computer vision. Although generative adversarial networks have made significant …

Despite the advance of intelligent fault diagnosis for rolling bearings, in industries, data-
driven methods still suffer from data acquisition and imbalance. We propose an enhanced …

The advent of deep neural networks and improved computational power have brought a
revolutionary transformation in the fields of computer vision and image processing. Within …

Text-to-image synthesis is a fundamental and challenging task in computer vision, which
aims to synthesize realistic images from given descriptions. Recently, text-to-image …

The success of deep learning methods hinges on the availability of large training datasets
annotated for the task of interest. In contrast to human intelligence, these methods lack …

Over the past decade, deep learning-based computer vision methods have been shown to
surpass previous state-of-the-art computer vision techniques in various fields, and have …

Since generative adversarial network (GAN) can learn data distribution and generate new
samples based on the learned data distribution, it has become a research hotspot in the …

Adversarial images generated by generative adversarial networks are not close to any
existing benign images, and contain nonrobust features that have been identified as critical …

Deep learning has shown its great potential in the field of image classification due to its
powerful feature extraction ability, which heavily depends on the number of available …