Search Results for author: Tianyun Zhang

Found 24 papers, 7 papers with code

EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV

no code implementations8 Mar 2024 Huiming Sun, Jiacheng Guo, Zibo Meng, Tianyun Zhang, Jianwu Fang, Yuewei Lin, Hongkai Yu

One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV.

Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection

no code implementations30 Jun 2023 Huiming Sun, Lan Fu, Jinlong Li, Qing Guo, Zibo Meng, Tianyun Zhang, Yuewei Lin, Hongkai Yu

Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model.

Adversarial Attack object-detection +2

Deep Transfer Learning for Intelligent Vehicle Perception: a Survey

no code implementations26 Jun 2023 Xinyu Liu, Jinlong Li, Jin Ma, Huiming Sun, Zhigang Xu, Tianyun Zhang, Hongkai Yu

To the best of our knowledge, this paper represents the first comprehensive survey on the topic of the deep transfer learning for intelligent vehicle perception.

Autonomous Driving Decision Making +2

Loss Attitude Aware Energy Management for Signal Detection

no code implementations18 Jan 2023 Baocheng Geng, Chen Quan, Tianyun Zhang, Makan Fardad, Pramod K. Varshney

The amount of resource consumption that maximizes the humans' subjective utility is derived to characterize the actual behavior of humans.

energy management Management

Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting

no code implementations21 Dec 2021 Minghai Qin, Tianyun Zhang, Fei Sun, Yen-Kuang Chen, Makan Fardad, Yanzhi Wang, Yuan Xie

Deep neural networks (DNNs) have shown to provide superb performance in many real life applications, but their large computation cost and storage requirement have prevented them from being deployed to many edge and internet-of-things (IoT) devices.

Graph Attention

Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks

no code implementations20 Dec 2021 Fei Sun, Minghai Qin, Tianyun Zhang, Xiaolong Ma, Haoran Li, Junwen Luo, Zihao Zhao, Yen-Kuang Chen, Yuan Xie

Our experiments show that GS patterns consistently make better trade-offs between accuracy and computation efficiency compared to conventional structured sparse patterns.

Machine Translation speech-recognition +1

Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search

no code implementations ICCV 2021 Zheng Zhan, Yifan Gong, Pu Zhao, Geng Yuan, Wei Niu, Yushu Wu, Tianyun Zhang, Malith Jayaweera, David Kaeli, Bin Ren, Xue Lin, Yanzhi Wang

Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices.

Image Super-Resolution Neural Architecture Search +1

Computation on Sparse Neural Networks: an Inspiration for Future Hardware

no code implementations24 Apr 2020 Fei Sun, Minghai Qin, Tianyun Zhang, Liu Liu, Yen-Kuang Chen, Yuan Xie

We show that for practically complicated problems, it is more beneficial to search large and sparse models in the weight dominated region.

A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

no code implementations12 Apr 2020 Tianyun Zhang, Xiaolong Ma, Zheng Zhan, Shanglin Zhou, Minghai Qin, Fei Sun, Yen-Kuang Chen, Caiwen Ding, Makan Fardad, Yanzhi Wang

To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i. e., static regularization-based pruning and dynamic regularization-based pruning.

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

no code implementations ECCV 2020 Xiaolong Ma, Wei Niu, Tianyun Zhang, Sijia Liu, Sheng Lin, Hongjia Li, Xiang Chen, Jian Tang, Kaisheng Ma, Bin Ren, Yanzhi Wang

Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms.

Code Generation Compiler Optimization

Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness

no code implementations25 Sep 2019 Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li

The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.

Adversarial Attack Adversarial Robustness

An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM

no code implementations29 Aug 2019 Geng Yuan, Xiaolong Ma, Caiwen Ding, Sheng Lin, Tianyun Zhang, Zeinab S. Jalali, Yilong Zhao, Li Jiang, Sucheta Soundarajan, Yanzhi Wang

Memristor-based weight pruning and weight quantization have been seperately investigated and proven effectiveness in reducing area and power consumption compared to the original DNN model.


Adversarial Attack Generation Empowered by Min-Max Optimization

1 code implementation NeurIPS 2021 Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li

In this paper, we show how a general framework of min-max optimization over multiple domains can be leveraged to advance the design of different types of adversarial attacks.

Adversarial Attack Adversarial Robustness

Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

2 code implementations23 Mar 2019 Shaokai Ye, Xiaoyu Feng, Tianyun Zhang, Xiaolong Ma, Sheng Lin, Zhengang Li, Kaidi Xu, Wujie Wen, Sijia Liu, Jian Tang, Makan Fardad, Xue Lin, Yongpan Liu, Yanzhi Wang

A recent work developed a systematic frame-work of DNN weight pruning using the advanced optimization technique ADMM (Alternating Direction Methods of Multipliers), achieving one of state-of-art in weight pruning results.

Model Compression Quantization

StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs

1 code implementation29 Jul 2018 Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Xiaolong Ma, Ning Liu, Linfeng Zhang, Jian Tang, Kaisheng Ma, Xue Lin, Makan Fardad, Yanzhi Wang

Without loss of accuracy on the AlexNet model, we achieve 2. 58X and 3. 65X average measured speedup on two GPUs, clearly outperforming the prior work.

Model Compression

A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers

3 code implementations ECCV 2018 Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, Yanzhi Wang

We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning.

Image Classification Network Pruning

Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers

1 code implementation15 Feb 2018 Tianyun Zhang, Shaokai Ye, Yi-Peng Zhang, Yanzhi Wang, Makan Fardad

We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM).

Computational Efficiency

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