8 citations · 16 across the 5 of their papers we have counts for
8 papers
Learning Hierarchical Graph Representation for Image Manipulation Detection
Wenyan Pan, Zhili Zhou, Miaogen Ling +2
The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural…
Instance-Dependent Partial Label Learning
Ning Xu, Congyu Qiao, Xin Geng +1
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true.…
Learning from Noisy Labels via Dynamic Loss Thresholding
Hao Yang, Youzhi Jin, Ziyin Li +4
Numerous researches have proved that deep neural networks (DNNs) can fit everything in the end even given data with noisy labels, and result in poor generalization performance. How…
Compact Learning for Multi-Label Classification
Jiaqi Lv, Tianran Wu, Chenglun Peng +3
Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC enc…
Provably Consistent Partial-Label Learning
Lei Feng, Jiaqi Lv, Bo Han +5
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL metho…
Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv, Miao Xu, Lei Feng +3
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the tr…