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Transfer Learning in General Lensless Imaging through Scattering Media
Yukuan Yang, Lei Deng, Peng Jiao +4
Recently deep neural networks (DNNs) have been successfully introduced to the field of lensless imaging through scattering media. By solving an inverse problem in computational ima…
Adversarial symmetric GANs: bridging adversarial samples and adversarial networks
Faqiang Liu, Mingkun Xu, Guoqi Li +3
Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve tra…
Compressing 3DCNNs Based on Tensor Train Decomposition
Dingheng Wang, Guangshe Zhao, Guoqi Li +2
Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks, e.g., video and 3D point cloud recognition. However, due to the higher dimension of convol…
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Lei Deng, Yujie Wu, Yifan Hu +6
As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with h…
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking
Zheyu Yang, Yujie Wu, Guanrui Wang +5
Computer-science-oriented artificial neural networks (ANNs) have achieved tremendous success in a variety of scenarios via powerful feature extraction and high-precision data opera…
Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers
Yukuan Yang, Shuang Wu, Lei Deng +3
Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simpl…
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Jibin Wu, Yansong Chua, Malu Zhang +3
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures. However, due to the non-differentia…
Deep Spiking Neural Network with Spike Count based Learning Rule
Jibin Wu, Yansong Chua, Malu Zhang +3
Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synch…