24 citations · 38 across the 5 of their papers we have counts for
5 papers
Deep Partial Multi-Label Learning with Graph Disambiguation
Haobo Wang, Shisong Yang, Gengyu Lyu +5
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. Rec…
GM-MLIC: Graph Matching based Multi-Label Image Classification
Yanan Wu, He Liu, Songhe Feng +3
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image conten…
HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
Gengyu Lyu, Songhe Feng, Yi Jin +3
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. Most existing m…
GM-PLL: Graph Matching based Partial Label Learning
Gengyu Lyu, Songhe Feng, Tao Wang +2
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. The key to deal…
A Self-paced Regularization Framework for Partial-Label Learning
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. Most PLL algori…