43 citations · 89 across the 14 of their papers we have counts for
14 papers
The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
Meiyu Huang, Yao Xu, Lixin Qian +6
Deep learning techniques have made an increasing impact on the field of remote sensing. However, deep neural networks based fusion of multimodal data from different remote sensors…
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches
Nan Ji, YanFei Feng, Haidong Xie +2
The security of object detection systems has attracted increasing attention, especially when facing adversarial patch attacks. Since patch attacks change the pixels in a restricted…
Boosting ship detection in SAR images with complementary pretraining techniques
Wei Bao, Meiyu Huang, Yaqin Zhang +3
Deep learning methods have made significant progress in ship detection in synthetic aperture radar (SAR) images. The pretraining technique is usually adopted to support deep neural…
A Neural Network with Plane Wave Activation for Helmholtz Equation
Ziming Wang, Tao Cui, Xueshuang Xiang
This paper proposes a plane wave activation based neural network (PWNN) for solving Helmholtz equation, the basic partial differential equation to represent wave propagation, e.g.…
An adaptive high-order unfitted finite element method for elliptic interface problems
Zhiming Chen, Ke Li, Xueshuang Xiang
We design an adaptive unfitted finite element method on the Cartesian mesh with hanging nodes. We derive an hp-reliable and efficient residual type a posteriori error estimate on K…
How Does GAN-based Semi-supervised Learning Work?
Xuejiao Liu, Xueshuang Xiang
Generative adversarial networks (GANs) have been widely used and have achieved competitive results in semi-supervised learning. This paper theoretically analyzes how GAN-based semi…
Blind Adversarial Training: Balance Accuracy and Robustness
Haidong Xie, Xueshuang Xiang, Naijin Liu +1
Adversarial training (AT) aims to improve the robustness of deep learning models by mixing clean data and adversarial examples (AEs). Most existing AT approaches can be grouped int…
Blind Adversarial Pruning: Balance Accuracy, Efficiency and Robustness
Haidong Xie, Lixin Qian, Xueshuang Xiang +1
With the growth of interest in the attack and defense of deep neural networks, researchers are focusing more on the robustness of applying them to devices with limited memory. Thus…
Training few-shot classification via the perspective of minibatch and pretraining
Meiyu Huang, Xueshuang Xiang, Yao Xu
Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data and has drawn considerable attention in machin…
Deep Domain Decomposition Method: Elliptic Problems
Wuyang Li, Xueshuang Xiang, Yingxiang Xu
This paper proposes a deep-learning-based domain decomposition method (DeepDDM), which leverages deep neural networks (DNN) to discretize the subproblems divided by domain decompos…
Towards GANs' Approximation Ability
Xuejiao Liu, Yao Xu, Xueshuang Xiang
Generative adversarial networks (GANs) have attracted intense interest in the field of generative models. However, few investigations focusing either on the theoretical analysis or…
Task-Driven Common Representation Learning via Bridge Neural Network
Yao Xu, Xueshuang Xiang, Meiyu Huang
This paper introduces a novel deep learning based method, named bridge neural network (BNN) to dig the potential relationship between two given data sources task by task. The propo…
Stochastic Model Pruning via Weight Dropping Away and Back
Haipeng Jia, Xueshuang Xiang, Da Fan +3
Deep neural networks have dramatically achieved great success on a variety of challenging tasks. However, most successful DNNs have an extremely complex structure, leading to exten…
Sparse Reconstructions of Acoustic Source for Inverse Scattering Problems in Measure Space
Xueshuang Xiang, Hongpeng Sun
This paper proposes a systematic mathematical analysis of both the direct and inverse acoustic scattering problem given the source in Radon measure space. For the direct problem, w…