46 citations · 52 across the 5 of their papers we have counts for
5 papers
BiaSwap: Removing dataset bias with bias-tailored swapping augmentation
Eungyeup Kim, Jihyeon Lee, Jaegul Choo
Deep neural networks often make decisions based on the spurious correlations inherent in the dataset, failing to generalize in an unbiased data distribution. Although previous appr…
Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects
Eungyeup Kim, Sanghyeon Lee, Jeonghoon Park +3
Deep neural networks for automatic image colorization often suffer from the color-bleeding artifact, a problematic color spreading near the boundaries between adjacent objects. Suc…
Learning Debiased Representation via Disentangled Feature Augmentation
Jungsoo Lee, Eungyeup Kim, Juyoung Lee +2
Image classification models tend to make decisions based on peripheral attributes of data items that have strong correlation with a target variable (i.e., dataset bias). These bias…
Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence
Junsoo Lee, Eungyeup Kim, Yunsung Lee +3
This paper tackles the automatic colorization task of a sketch image given an already-colored reference image. Colorizing a sketch image is in high demand in comics, animation, and…
Unpaired Image Translation via Adaptive Convolution-based Normalization
Wonwoong Cho, Kangyeol Kim, Eungyeup Kim +2
Disentangling content and style information of an image has played an important role in recent success in image translation. In this setting, how to inject given style into an inpu…