no code implementations • 2 Jul 2023 • Shuo He, Lei Feng, Guowu Yang
In this paper, we term the examples whose true label is outside the candidate label set OOC (out-of-candidate) examples, and pioneer a new PLL study to learn with OOC examples.
1 code implementation • 12 Jun 2023 • Senlin Shu, Shuo He, Haobo Wang, Hongxin Wei, Tao Xiang, Lei Feng
In this paper, we propose a generalized URE that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees, given unlabeled data for LAC.
no code implementations • ICCV 2023 • Shuo He, Guowu Yang, Lei Feng
In this paper, we start with an empirical study of the dynamics of label disambiguation in both II-PLL and ID-PLL.
no code implementations • 17 Apr 2020 • Senlin Shu, Fengmao Lv, Yan Yan, Li Li, Shuo He, Jun He
In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning.
no code implementations • 8 Feb 2019 • Lei Feng, Bo An, Shuo He
It is well-known that exploiting label correlations is crucially important to multi-label learning.