The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, ie,
model performance deterioration on past tasks when learning a new task. However, the …

We propose a novel e dge guided g enerative a dversarial n etwork with c ontrastive
learning (ECGAN) for the challenging semantic image synthesis task. Although considerable …

With the rapid development of AI hardware accelerators, applying deep learning-based
algorithms to solve various low-level vision tasks on mobile devices has gradually become …

Recently, deep learning based image super-resolution (SR) models show a strong
performance thanks to the convolution neural network (CNN). However, these CNN-based …

Object detection on the edge (Edge-OD) is in growing demand thanks to its ever-broad
application prospects. However, the development of this field is rigorously restricted by the …

Traditional Convolutional Neural Network (ConvNet, CNN)-based image super-resolution
(SR) methods have lower computation costs, making them more friendly for real-world …

We propose a real-time convolutional neural network (CNN) training and compression
method for delivering high-quality live video even in a poor network environment. The server …

Vision Transformer (ViT)-based image super-resolution (SR) methods have achieved
impressive performance and surpassed CNN-based SR methods by utilizing Multi-Head …

With the emergence of DNN applications on mobile devices, plenty of attention has been
attracted to their optimization. However, the impact of DNN inference tasks on device power …