Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Click-through rate (CTR) prediction, whose goal is to estimate the probability of a user
clicking on the item, has become one of the core tasks in the advertising system. For CTR …

The key challenge of knowledge distillation is to extract general, moderate and sufficient
knowledge from a teacher network to guide a student network. In this paper, a novel …

Distillation-based learning boosts the performance of the miniaturized neural network based
on the hypothesis that the representation of a teacher model can be used as structured and …

Typically, the deployment of face recognition models in the wild needs to identify low-
resolution faces with extremely low computational cost. To address this problem, a feasible …

Human pose estimation aims at localizing human anatomical keypoints or body parts in the
input data (eg, images, videos, or signals). It forms a crucial component in enabling …

Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …