Search Results for author: Cheng-Zhong Xu

Found 23 papers, 7 papers with code

SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing

no code implementations24 Oct 2021 Kafeng Wang, Haoyi Xiong, Jie Zhang, Hongyang Chen, Dejing Dou, Cheng-Zhong Xu

Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i. e., 7 types by SenseMag versus 4 types by the existing work in comparisons).

Federated Noisy Client Learning

no code implementations24 Jun 2021 Li Li, Huazhu Fu, Bo Han, Cheng-Zhong Xu, Ling Shao

To learn with noisy clients, we propose a simple yet effective FL framework, named Federated Noisy Client Learning (Fed-NCL), which is a plug-and-play algorithm and contains two main components: a data quality measurement (DQM) to dynamically quantify the data quality of each participating client, and a noise robust aggregation (NRA) to adaptively aggregate the local models of each client by jointly considering the amount of local training data and the data quality of each client.

Federated Learning

LAFEAT: Piercing Through Adversarial Defenses with Latent Features

1 code implementation CVPR 2021 Yunrui Yu, Xitong Gao, Cheng-Zhong Xu

In this paper, we show that latent features in certain "robust" models are surprisingly susceptible to adversarial attacks.

Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

1 code implementation CVPR 2021 Abulikemu Abuduweili, Xingjian Li, Humphrey Shi, Cheng-Zhong Xu, Dejing Dou

To better exploit the value of both pre-trained weights and unlabeled target examples, we introduce adaptive consistency regularization that consists of two complementary components: Adaptive Knowledge Consistency (AKC) on the examples between the source and target model, and Adaptive Representation Consistency (ARC) on the target model between labeled and unlabeled examples.

Transfer Learning

Frontier Detection and Reachability Analysis for Efficient 2D Graph-SLAM Based Active Exploration

1 code implementation7 Sep 2020 Zezhou Sun, Banghe Wu, Cheng-Zhong Xu, Sanjay E. Sarma, Jian Yang, Hui Kong

We propose an integrated approach to active exploration by exploiting the Cartographer method as the base SLAM module for submap creation and performing efficient frontier detection in the geometrically co-aligned submaps induced by graph optimization.

XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup

no code implementations20 Jul 2020 Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou

While the existing multitask learning algorithms need to run backpropagation over both the source and target datasets and usually consume a higher gradient complexity, XMixup transfers the knowledge from source to target tasks more efficiently: for every class of the target task, XMixup selects the auxiliary samples from the source dataset and augments training samples via the simple mixup strategy.

Transfer Learning

RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr

1 code implementation ICML 2020 Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou

RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning, while the effects of randomization can be easily converged throughout the overall learning procedure.

Transfer Learning

How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey

no code implementations24 May 2020 Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Cheng-Zhong Xu

Recently, various novel deep learning techniques have been developed to process graph data, called graph neural networks (GNNs).

Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction

1 code implementation11 May 2020 Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Cheng-Zhong Xu

However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways.

Stock Prediction

Pay Attention to Features, Transfer Learn Faster CNNs

no code implementations ICLR 2020 Kafeng Wang, Xitong Gao, Yiren Zhao, Xingjian Li, Dejing Dou, Cheng-Zhong Xu

Deep convolutional neural networks are now widely deployed in vision applications, but a limited size of training data can restrict their task performance.

Transfer Learning

LiDAR Iris for Loop-Closure Detection

no code implementations9 Dec 2019 Ying Wang, Zezhou Sun, Cheng-Zhong Xu, Sanjay Sarma, Jian Yang, Hui Kong

In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection.

Loop Closure Detection

A Robust Stereo Camera Localization Method with Prior LiDAR Map Constrains

no code implementations2 Dec 2019 Dong Han, Zuhao Zou, Lujia Wang, Cheng-Zhong Xu

Different from the conventional visual localization system, we design a novel visual optimization model by matching planar information between the LiDAR map and visual image.

Camera Localization Visual Localization

Focused Quantization for Sparse CNNs

1 code implementation NeurIPS 2019 Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, Cheng-Zhong Xu

In ResNet-50, we achieved a 18. 08x CR with only 0. 24% loss in top-5 accuracy, outperforming existing compression methods.

Neural Network Compression Quantization

Sitatapatra: Blocking the Transfer of Adversarial Samples

no code implementations23 Jan 2019 Ilia Shumailov, Xitong Gao, Yiren Zhao, Robert Mullins, Ross Anderson, Cheng-Zhong Xu

Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision.

General Classification

Dynamic Channel Pruning: Feature Boosting and Suppression

2 code implementations ICLR 2019 Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, Cheng-Zhong Xu

Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources.

Model Compression Network Pruning

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