1 citations · 1 across the 3 of their papers we have counts for
5 papers · 1 filter
Local Magnification for Data and Feature Augmentation
Kun He, Chang Liu, Stephen Lin +1
In recent years, many data augmentation techniques have been proposed to increase the diversity of input data and reduce the risk of overfitting on deep neural networks. In this wo…
Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels
Daizong Liu, Shuangjie Xu, Pan Zhou +3
Disease diagnosis on chest X-ray images is a challenging multi-label classification task. Previous works generally classify the diseases independently on the input image without co…
Single Image Reflection Removal through Cascaded Refinement
Chao Li, Yixiao Yang, Kun He +2
We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important proble…
Robust Local Features for Improving the Generalization of Adversarial Training
Chuanbiao Song, Kun He, Jiadong Lin +2
Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples. However, adversarially trained mo…
Child Gender Determination with Convolutional Neural Networks on Hand Radio-Graphs
Mumtaz A. Kaloi, Kun He
Motivation: In forensic or medico-legal investigation as well as in anthropology the gender determination of the subject (hit by a disastrous or any kind of traumatic situation) is…