22 citations · 40 across the 5 of their papers we have counts for
6 papers
Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame
Youngjoon Yu, Hong Joo Lee, Hakmin Lee +1
Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of pe…
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
Hong Joo Lee, Seong Tae Kim, Hakmin Lee +2
Recent studies have shown that ensemble approaches could not only improve accuracy and but also estimate model uncertainty in deep learning. However, it requires a large number of…
Revisiting Role of Autoencoders in Adversarial Settings
Byeong Cheon Kim, Jung Uk Kim, Hakmin Lee +1
To combat against adversarial attacks, autoencoder structure is widely used to perform denoising which is regarded as gradient masking. In this paper, we revisit the role of autoen…
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
Hakmin Lee, Hong Joo Lee, Seong Tae Kim +1
Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not re…
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation
Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee +1
This paper deals with a method for generating realistic labeled masses. Recently, there have been many attempts to apply deep learning to various bio-image computing fields includi…
ICADx: Interpretable computer aided diagnosis of breast masses
Seong Tae Kim, Hakmin Lee, Hak Gu Kim +1
In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has…