Learning from True-False Labels via Multi-modal Prompt Retrieving

1 code implementation • 24 May 2024 • Zhongnian Li, Jinghao Xu, Peng Ying, Meng Wei, Tongfeng Sun, Xinzheng Xu

Weakly supervised learning has recently achieved considerable success in reducing annotation costs and label noise.

Multi-label Learning from Privacy-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.)

Multi-Label Learning

Learning from Positive and Unlabeled Data with Augmented Classes

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.