7 citations · 19 across the 7 of their papers we have counts for
7 papers
Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
Qigong Sun, Xiufang Li, Fanhua Shang +4
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage. Thus, DNNs cannot be efficiently applied to mobile phones and…
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment
Qigong Sun, Xiufang Li, Yan Ren +4
As an effective technique to achieve the implementation of deep neural networks in edge devices, model quantization has been successfully applied in many practical applications. No…
MWQ: Multiscale Wavelet Quantized Neural Networks
Qigong Sun, Yan Ren, Licheng Jiao +3
Model quantization can reduce the model size and computational latency, it has become an essential technique for the deployment of deep neural networks on resourceconstrained hardw…
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization
Qigong Sun, Licheng Jiao, Yan Ren +3
Since model quantization helps to reduce the model size and computation latency, it has been successfully applied in many applications of mobile phones, embedded devices and smart…
Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification
Xiufang Li, Qigong Sun, Lingling Li +3
Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs). In this paper, based on the mechanism of the popula…
Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification
Qigong Sun, Xiufang Li, Lingling Li +3
Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some challeng…
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
Qigong Sun, Fanhua Shang, Kang Yang +3
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance. Thus, DNNs cannot be efficiently applied to mobile phones…