38 citations · 114 across the 6 of their papers we have counts for
6 papers
TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples
Jiahui Cheng, Bin Guo, Jiaqi Liu +4
Surface defect detection plays an increasingly important role in manufacturing industry to guarantee the product quality. Many deep learning methods have been widely used in surfac…
AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications
Sicong Liu, Bin Guo, Ke Ma +2
There are many deep learning (e.g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of hu…
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy
Sicong Liu, Junzhao Du, Anshumali Shrivastava +1
The remarkable success of machine learning has fostered a growing number of cloud-based intelligent services for mobile users. Such a service requires a user to send data, e.g. ima…
AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles
Sicong Liu, Junzhao Du, Kaiming Nan +3
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of de…
Eliminating NB-IoT Interference to LTE System: a Sparse Machine Learning Based Approach
Sicong Liu, Liang Xiao, Zhu Han +1
Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to ex…
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network
Sicong Liu, Anshumali Shrivastava, Junzhao Du +1
The remarkable success of machine learning, especially deep learning, has produced a variety of cloud-based services for mobile users. Such services require an end user to send dat…