498 citations · 2.5k across the 114 of their papers we have counts for
31 papers · 1 filter
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
Qing Jin, Jian Ren, Richard Zhuang +6
Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there…
SPViT: Enabling Faster Vision Transformers via Soft Token Pruning
Zhenglun Kong, Peiyan Dong, Xiaolong Ma +9
Recently, Vision Transformer (ViT) has continuously established new milestones in the computer vision field, while the high computation and memory cost makes its propagation in ind…
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers
Husheng Han, Kaidi Xu, Xing Hu +6
Adversarial patch attacks that craft the pixels in a confined region of the input images show their powerful attack effectiveness in physical environments even with noises or defor…
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks
Cheng Gong, Ye Lu, Kunpeng Xie +3
Quantization has been proven to be a vital method for improving the inference efficiency of deep neural networks (DNNs). However, it is still challenging to strike a good balance b…
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search
Pu Zhao, Wei Niu, Geng Yuan +4
Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to diff…
Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices
Xuan Shen, Geng Yuan, Wei Niu +5
The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, e…
Effective Model Sparsification by Scheduled Grow-and-Prune Methods
Xiaolong Ma, Minghai Qin, Fei Sun +7
Deep neural networks (DNNs) are effective in solving many real-world problems. Larger DNN models usually exhibit better quality (e.g., accuracy) but their excessive computation res…
Teachers Do More Than Teach: Compressing Image-to-Image Models
Qing Jin, Jian Ren, Oliver J. Woodford +4
Generative Adversarial Networks (GANs) have achieved huge success in generating high-fidelity images, however, they suffer from low efficiency due to tremendous computational cost…
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device
Pu Zhao, Wei Niu, Geng Yuan +7
3D object detection is an important task, especially in the autonomous driving application domain. However, it is challenging to support the real-time performance with the limited…
ClickTrain: Efficient and Accurate End-to-End Deep Learning Training via Fine-Grained Architecture-Preserving Pruning
Chengming Zhang, Geng Yuan, Wei Niu +8
Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-linear because of the growing demand on prediction accuracy and analysis quality. The wide and…
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search
Yushuo Guan, Ning Liu, Pengyu Zhao +4
The convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment. Structured (channel…
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
Yuxuan Cai, Hongjia Li, Geng Yuan +5
The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-a…
AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency
Fuxun Yu, Chenchen Liu, Di Wang +2
Convolutional Neural Networks (CNNs) achieved great cognitive performance at the expense of considerable computation load. To relieve the computation load, many optimization works…
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space
Mohammad Saeed Abrishami, Amir Erfan Eshratifar, David Eigen +3
Recent advances in the field of artificial intelligence have been made possible by deep neural networks. In applications where data are scarce, transfer learning and data augmentat…
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Xiaolong Ma, Wei Niu, Tianyun Zhang +8
Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on vario…
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
Yue Niu, Hanqing Zeng, Ajitesh Srivastava +4
To accelerate inference of Convolutional Neural Networks (CNNs), various techniques have been proposed to reduce computation redundancy. Converting convolutional layers into freque…
Adversarial T-shirt! Evading Person Detectors in A Physical World
Kaidi Xu, Gaoyuan Zhang, Sijia Liu +6
It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-called physical adversarial examples deceive DNN-based decisionmakers by attaching advers…
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs
Caiwen Ding, Shuo Wang, Ning Liu +3
Deep neural networks (DNNs), as the basis of object detection, will play a key role in the development of future autonomous systems with full autonomy. The autonomous systems have…
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Adnan Siraj Rakin, Zhezhi He, Li Yang +3
Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka. adversarial attack. As one of the counte…
Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
Hao Tang, Dan Xu, Nicu Sebe +3
Cross-view image translation is challenging because it involves images with drastically different views and severe deformation. In this paper, we propose a novel approach named Mul…
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
Zihao Liu, Xiaowei Xu, Tao Liu +7
Cloud based medical image analysis has become popular recently due to the high computation complexities of various deep neural network (DNN) based frameworks and the increasingly l…
Adversarial Robustness vs Model Compression, or Both?
Shaokai Ye, Kaidi Xu, Sijia Liu +6
It is well known that deep neural networks (DNNs) are vulnerable to adversarial attacks, which are implemented by adding crafted perturbations onto benign examples. Min-max robust…
CircConv: A Structured Convolution with Low Complexity
Siyu Liao, Zhe Li, Liang Zhao +3
Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications. However, the lar…
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Zhe Li, Caiwen Ding, Siyue Wang +8
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types…