no code implementations • 28 Mar 2024 • Yuhang Li, Xin Dong, Chen Chen, Jingtao Li, Yuxin Wen, Michael Spranger, Lingjuan Lyu
Synthetic image data generation represents a promising avenue for training deep learning models, particularly in the realm of transfer learning, where obtaining real images within a specific domain can be prohibitively expensive due to privacy and intellectual property considerations.
1 code implementation • 11 Oct 2023 • Jingtao Li, Xinyu Wang, Hengwei Zhao, Liangpei Zhang, Yanfei Zhong
Firstly, we reformulate the anomaly detection task as an undirected bilayer graph based on the deviation relationship, where the anomaly score is modeled as the conditional probability, given the pattern of the background and normal objects.
1 code implementation • ICCV 2023 • Hengwei Zhao, Xinyu Wang, Jingtao Li, Yanfei Zhong
Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications.
1 code implementation • 22 Mar 2023 • Jingtao Li, Xinyu Wang, Shaoyu Wang, Hengwei Zhao, Liangpei Zhang, Yanfei Zhong
In this paper, an unsupervised transferred direct detection (TDD) model is proposed, which is optimized directly for the anomaly detection task (one-step paradigm) and has transferability.
no code implementations • 13 Mar 2023 • Jingtao Li, Adnan Siraj Rakin, Xing Chen, Li Yang, Zhezhi He, Deliang Fan, Chaitali Chakrabarti
We show that under practical cases, the proposed ME attacks work exceptionally well for SFL.
1 code implementation • 31 Jan 2023 • Jingtao Li, Xinyu Wang, Hengwei Zhao, Shaoyu Wang, Yanfei Zhong
Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications.
no code implementations • 4 Oct 2022 • Jingtao Li, Runcong Kuang
To prevent this, Federated Learning is proposed as a private learning scheme, using which users can locally train the model without collecting users' raw data to servers.
1 code implementation • 18 Aug 2022 • Jingtao Li, Jian Zhou, Yan Xiong, Xing Chen, Chaitali Chakrabarti
Sampling is an essential part of raw point cloud data processing such as in the popular PointNet++ scheme.
1 code implementation • CVPR 2022 • Jingtao Li, Adnan Siraj Rakin, Xing Chen, Zhezhi He, Deliang Fan, Chaitali Chakrabarti
While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server.
no code implementations • 20 Jul 2021 • Xing Chen, Jingtao Li, Chaitali Chakrabarti
An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates.
no code implementations • 22 Mar 2021 • Adnan Siraj Rakin, Li Yang, Jingtao Li, Fan Yao, Chaitali Chakrabarti, Yu Cao, Jae-sun Seo, Deliang Fan
Apart from recovering the inference accuracy, our RA-BNN after growing also shows significantly higher resistance to BFA.
1 code implementation • 20 Jan 2021 • Jingtao Li, Adnan Siraj Rakin, Zhezhi He, Deliang Fan, Chaitali Chakrabarti
In this work, we propose RADAR, a Run-time adversarial weight Attack Detection and Accuracy Recovery scheme to protect DNN weights against PBFA.
2 code implementations • 24 Jul 2020 • Adnan Siraj Rakin, Zhezhi He, Jingtao Li, Fan Yao, Chaitali Chakrabarti, Deliang Fan
Prior works of BFA focus on un-targeted attack that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory.