Dubbed “the successor to the mobile Internet,” the concept of the Metaverse has grown in
popularity. While there exist lite versions of the Metaverse today, they are still far from …

A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …

An important paradigm of natural language processing consists of large-scale pre-training
on general domain data and adaptation to particular tasks or domains. As we pre-train larger …

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 …

Current network architecture design is mostly guided by the indirect metric of computation
complexity, ie, FLOPs. However, the direct metric, such as speed, also depends on the other …

Light-weight convolutional neural networks (CNNs) are specially designed for applications
on mobile devices with faster inference speed. The convolutional operation can only capture …

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which
is designed specially for mobile devices with very limited computing power (eg, 10-150 …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Convolutional neural networks are built upon the convolution operation, which extracts
informative features by fusing spatial and channel-wise information together within local …

We present a simple, highly modularized network architecture for image classification. Our
network is constructed by repeating a building block that aggregates a set of transformations …