no code implementations • 25 Mar 2024 • Meng Wei, Zhongnian Li, Yong Zhou, Xinzheng Xu
Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and time-consuming.
no code implementations • 25 Mar 2024 • Meng Wei, Zhongnian Li, Peng Ying, Yong Zhou, Xinzheng Xu
In this novel labeling setting, each training instance is associated with a \textit{determined label} (either "Yes" or "No"), which indicates whether the training instance contains the provided class label.
no code implementations • 20 Dec 2023 • Zhongnian Li, Haotian Ren, Tongfeng Sun, Zhichen Li
Multi-abel Learning (MLL) often involves the assignment of multiple relevant labels to each instance, which can lead to the leakage of sensitive information (such as smoking, diseases, etc.)
no code implementations • 1 Feb 2023 • Meng Wei, Zhongnian Li, Yong Zhou, Qiaoyu Guo, Xinzheng Xu
Annotating multi-class instances is a crucial task in the field of machine learning.
no code implementations • 19 Nov 2022 • Zhongnian Li, Jian Zhang, Mengting Xu, Xinzheng Xu, Daoqiang Zhang
In this paper, we propose a novel problem setting called Complementary Labels Learning with Augmented Classes (CLLAC), which brings the challenge that classifiers trained by complementary labels should not only be able to classify the instances from observed classes accurately, but also recognize the instance from the Augmented Classes in the testing phase.
no code implementations • 28 Sep 2022 • Meng Wei, Yong Zhou, Zhongnian Li, Xinzheng Xu
In such scenarios, the number of samples in one class is considerably lower than in other classes, which consequently leads to a decline in the accuracy of predictions.
no code implementations • 27 Jul 2022 • Zhongnian Li, Liutao Yang, Zhongchen Ma, Tongfeng Sun, Xinzheng Xu, Daoqiang Zhang
In this paper, we propose an unbiased risk estimator for PU learning with Augmented Classes (PUAC) by utilizing unlabeled data from the augmented classes distribution, which can be easily collected in many real-world scenarios.
no code implementations • 23 Jun 2022 • Mengting Xu, Tao Zhang, Zhongnian Li, Daoqiang Zhang
Therefore, guaranteeing the robustness of hard examples is crucial for improving the final robustness of the model.
no code implementations • 7 Apr 2022 • Liutao Yang, Zhongnian Li, Rongjun Ge, Junyong Zhao, Haipeng Si, Daoqiang Zhang
Furthermore, in order to improve the performance in the image domain, we propose the image reconstruction module to complementarily denoise both in the sinogram and image domain.
no code implementations • 29 Jan 2022 • Mengting Xu, Tao Zhang, Zhongnian Li, Daoqiang Zhang
Further, we propose Scale-Invariant (SI) adversarial defense mechanism based on the cosine angle matrix, which can be embedded into the popular adversarial defenses.
1 code implementation • 5 Mar 2021 • Mengting Xu, Tao Zhang, Zhongnian Li, Mingxia Liu, Daoqiang Zhang
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images.
no code implementations • 24 Dec 2020 • Mengting Xu, Tao Zhang, Zhongnian Li, Daoqiang Zhang
A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to be effective to train neural networks that are certifiably robust to the attacker.
no code implementations • 18 Feb 2019 • Zhongnian Li, Tao Zhang, Peng Wan, Daoqiang Zhang
Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI).