53 citations · 92 across the 11 of their papers we have counts for
12 papers
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…
Enhancing the Transferability of Adversarial Attacks through Variance Tuning
Xiaosen Wang, Kun He
Deep neural networks are vulnerable to adversarial examples that mislead the models with imperceptible perturbations. Though adversarial attacks have achieved incredible success ra…
Local Generalization and Bucketization Technique for Personalized Privacy Preservation
Boyu Li, Kun He, Geng Sun
Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. In most previous approaches, a microdata table consists of three cat…
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…
Sparse Nonnegative Matrix Factorization for Multiple Local Community Detection
Dany Kamuhanda, Meng Wang, Kun He
Local community detection consists of finding a group of nodes closely related to the seeds, a small set of nodes of interest. Such group of nodes are densely connected or have a h…
Hidden Community Detection on Two-layer Stochastic Models: a Theoretical Perspective
Jialu Bao, Kun He, Xiaodong Xin +2
Hidden community is a new graph-theoretical concept recently proposed [4], in which the authors also propose a meta-approach called HICODE (Hidden Community Detection) for detectin…