activity
20222024
most citedA Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20241 cited

Object Style Diffusion for Generalized Object Detection in Urban Scene

Hao Li, Xiangyuan Yang, Mengzhu Wang +4

Object detection is a critical task in computer vision, with applications in various domains such as autonomous driving and urban scene monitoring. However, deep learning-based app…

cs.AI2024

Adversarial Detection with a Dynamically Stable System

Xiaowei Long, Jie Lin, Xiangyuan Yang

Adversarial detection is designed to identify and reject maliciously crafted adversarial examples(AEs) which are generated to disrupt the classification of target models. Presently…

cs.LG2023

Fuzziness-tuned: Improving the Transferability of Adversarial Examples

Xiangyuan Yang, Jie Lin, Hanlin Zhang +2

With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considera…

cs.SD20231 cited

A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments

Zhaoxi Mu, Xinyu Yang, Xiangyuan Yang +1

In noisy and reverberant environments, the performance of deep learning-based speech separation methods drops dramatically because previous methods are not designed and optimized f…

cs.LG2022

Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction

Xiangyuan Yang, Jie Lin, Hanlin Zhang +2

Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model aga…