8 papers
Latent Geometric Chords for Query-Efficient Decision-Based Adversarial Attacks
Ei Hmue Khine, Yao Li, Jiebao Sun +3
While decision-based black-box adversarial attacks present a severe security threat, current methodologies suffer from fundamental limitations. Pixel-wise attacks frequently introd…
PINNsFailureRegion Localization and Refinement through White-box AdversarialAttack
Shengzhu Shi, Yao Li, Zhichang Guo +2
Physics-informed neural networks (PINNs) have shown great promise in solving partial differential equations (PDEs). However, vanilla PINNs often face challenges when solving comple…
PDAC: Efficient Coreset Selection for Continual Learning via Probability Density Awareness
Junqi Gao, Zhichang Guo, Dazhi Zhang +3
Rehearsal-based Continual Learning (CL) maintains a limited memory buffer to store replay samples for knowledge retention, making these approaches heavily reliant on the quality of…
Towards Frequency-Adaptive Learning for SAR Despeckling
Ziqing Ma, Chang Yang, Zhichang Guo +1
Synthetic Aperture Radar (SAR) images are inherently corrupted by speckle noise, limiting their utility in high-precision applications. While deep learning methods have shown promi…
Progressive Alignment Degradation Learning for Pansharpening
Enzhe Zhao, Zhichang Guo, Yao Li +2
Deep learning-based pansharpening has been shown to effectively generate high-resolution multispectral (HRMS) images. To create supervised ground-truth HRMS images, synthetic data…
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation
Yi Ran, Zhichang Guo, Jia Li +3
The removal of multiplicative Gamma noise is a critical research area in the application of synthetic aperture radar (SAR) imaging, where neural networks serve as a potent tool. Ho…