Search Results for author: Zhongnian Li

Found 13 papers, 1 papers with code

Learning from Reduced Labels for Long-Tailed Data

no code implementations25 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.

Weakly-supervised Learning

Determined Multi-Label Learning via Similarity-Based Prompt

no code implementations25 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.

Multi-Label Classification Multi-Label Learning

Multi-label Learning from Privacy-Label

no code implementations20 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.)

Multi-Label Learning

Learning from Stochastic Labels

no code implementations1 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.

Complementary Labels Learning with Augmented Classes

no code implementations19 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.

Class-Imbalanced Complementary-Label Learning via Weighted Loss

no code implementations28 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.

Multi-class Classification Weakly Supervised Classification

Learning from Positive and Unlabeled Data with Augmented Classes

no code implementations27 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.

InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle

no code implementations23 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.

Low-Dose CT Denoising via Sinogram Inner-Structure Transformer

no code implementations7 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.

Denoising Image Reconstruction

Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses

no code implementations29 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.

Adversarial Attack Adversarial Defense

Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack

1 code implementation5 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.

Adversarial Attack Multi-Label Classification

Improving the Certified Robustness of Neural Networks via Consistency Regularization

no code implementations24 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.

SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction

no code implementations18 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).

Generative Adversarial Network MRI Reconstruction

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