no code implementations • 10 May 2023 • Haobo Wang, Shisong Yang, Gengyu Lyu, Weiwei Liu, Tianlei Hu, Ke Chen, Songhe Feng, Gang Chen
In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels.
no code implementations • 30 Apr 2021 • Yanan Wu, He Liu, Songhe Feng, Yi Jin, Gengyu Lyu, Zizhang Wu
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image.
no code implementations • 3 Jun 2019 • Gengyu Lyu, Songhe Feng, Yi Jin, Guojun Dai, Congyan Lang, Yidong Li
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct.
no code implementations • 10 Jan 2019 • Gengyu Lyu, Songhe Feng, Tao Wang, Congyan Lang, Yidong Li
Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct.
no code implementations • 20 Apr 2018 • Gengyu Lyu, Songhe Feng, Congyang Lang
Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label.