Neural Networks with Model Compression

Baochang Zhang is a full Professor with Institute of Artificial Intelligence, Beihang University, Beijing, China. He was selected by the Program for New Century Excellent Talents in University of Ministry of Education of China, also selected as Academic Advisor of Deep Learning Lab of Baidu Inc., and a distinguished researcher of Beihang Hangzhou Institute in Zhejiang Province. His research interests include explainable deep learning, computer vision and patter recognition. His HGPP and LDP methods were state-of-the-art feature descriptors, with 1234 and 768 Google Scholar citations, respectively. Both are “Test-of-Time” works. Our 1-bit methods achieved the best performance on ImageNet. His group also won the ECCV 2020 tiny object detection, COCO object detection, and ICPR 2020 Pollen recognition challenges. Tiancheng Wang are pursuing their Ph.D. degrees under the supervision of Baochang Zhang. His research topics include model compression and trustworthy deep learning, and he has published several high-quality papers on deep model compression. He was selected as visiting student of Zhongguancun laboratory, Beijing, China.   Sheng Xu are pursuing their Ph.D. degrees under the supervision of Baochang Zhang. His research topics mainly focus on low-bit model compression, and he is one of the most active researchers in the field of binary neural networks. He has published more than 10 top-tier papers in computer vision with two of them are selected as CVPR oral papers.  Dr. David Doermann is a Professor of Empire Innovation at the University at Buffalo (UB) and the Director of the University at Buffalo Artificial Intelligence Institute. Prior to coming to UB, he was a program manager at the Defense Advanced Research Projects Agency (DARPA), where he developed, selected and oversaw approximately $150 million in research and transition funding in the areas ofcomputer vision, human language technologies and voice analytics. He coordinated performers on all of the projects, orchestrating consensus, evaluating cross team management and overseeing fluid program objectives.

• Chapter 1. Introduction.- Chapter 2. Binary Neural Networks.- Chapter 3. Binary Neural Architecture Search.- Chapter 4. Quantization of Neural Networks.- Chapter 5. Network Pruning.- Chapter 6. Applications.