no code implementations • 23 Feb 2024 • Xinwen Cheng, Zhehao Huang, Xiaolin Huang
Machine Unlearning (MU) is to forget data from a well-trained model, which is practically important due to the "right to be forgotten".
1 code implementation • 11 Nov 2023 • Zhehao Huang, Tao Li, Chenhe Yuan, Yingwen Wu, Xiaolin Huang
Online continual learning is a challenging problem where models must learn from a non-stationary data stream while avoiding catastrophic forgetting.
1 code implementation • 26 May 2022 • Tao Li, Zhehao Huang, Yingwen Wu, Zhengbao He, Qinghua Tao, Xiaolin Huang, Chih-Jen Lin
Training deep neural networks (DNNs) in low-dimensional subspaces is a promising direction for achieving efficient training and better generalization performance.
1 code implementation • 24 May 2022 • Sizhe Chen, Zhehao Huang, Qinghua Tao, Yingwen Wu, Cihang Xie, Xiaolin Huang
The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores.
2 code implementations • 31 May 2021 • Sizhe Chen, Zhehao Huang, Qinghua Tao, Xiaolin Huang
Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial attacks, while the existing black-box attacks require extensive queries on the victim DNN to achieve high success rates.