Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Text-to-image diffusion models can create stunning images from natural language
descriptions that rival the work of professional artists and photographers. However, these …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Recently, pure transformer-based models have shown great potentials for vision tasks such
as image classification and detection. However, the design of transformer networks is …

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the
limited memory and computation resources. The redundancy in feature maps is an important …

Many modern object detectors demonstrate outstanding performances by using the
mechanism of looking and thinking twice. In this paper, we explore this mechanism in the …

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their
low computational budgets constrain both the depth (number of convolution layers) and the …

Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of
neural networks. There has been a flurry of algorithms that try to solve this practical problem …

Convolutional neural networks have been widely deployed in various application scenarios.
In order to extend the applications' boundaries to some accuracy-crucial domains …