no code implementations • 29 Dec 2021 • Guoliang Dong, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay, Xinyu Wang, Ting Dai, Jie Shi, Jin Song Dong
Furthermore, such attacks are impossible to eliminate, i. e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training.
no code implementations • 18 Jul 2021 • Bing Sun, Jun Sun, Ting Dai, Lijun Zhang
Our approach has been evaluated with multiple models trained on benchmark datasets and the experiment results show that our approach is effective and efficient.
no code implementations • 17 Jul 2021 • Peixin Zhang, Jingyi Wang, Jun Sun, Xinyu Wang, Guoliang Dong, Xingen Wang, Ting Dai, Jin Song Dong
In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminatory samples while extending existing fairness testing approaches to address a more challenging domain, i. e., text classification.
1 code implementation • 23 Feb 2021 • Xiao Li, Jianmin Li, Ting Dai, Jie Shi, Jun Zhu, Xiaolin Hu
A detection model based on the classification model EfficientNet-B7 achieved a top-1 accuracy of 53. 95%, surpassing previous state-of-the-art classification models trained on ImageNet, suggesting that accurate localization information can significantly boost the performance of classification models on ImageNet-A.
no code implementations • 3 Dec 2020 • Guoliang Dong, Jun Sun, Jingyi Wang, Xinyu Wang, Ting Dai
Neural networks are increasingly applied to support decision making in safety-critical applications (like autonomous cars, unmanned aerial vehicles and face recognition based authentication).
1 code implementation • 22 Sep 2019 • Guoliang Dong, Jingyi Wang, Jun Sun, Yang Zhang, Xinyu Wang, Ting Dai, Jin Song Dong, Xingen Wang
In this work, we propose an approach to extract probabilistic automata for interpreting an important class of neural networks, i. e., recurrent neural networks.