22 citations · 40 across the 5 of their papers we have counts for
5 papers
Highway Driving Dataset for Semantic Video Segmentation
Byungju Kim, Junho Yim, Junmo Kim
Scene understanding is an essential technique in semantic segmentation. Although there exist several datasets that can be used for semantic segmentation, they are mainly focused on…
Collaborative Method for Incremental Learning on Classification and Generation
Byungju Kim, Jaeyoung Lee, Kyungsu Kim +2
Although well-trained deep neural networks have shown remarkable performance on numerous tasks, they rapidly forget what they have learned as soon as they begin to learn with addit…
Adjusting Decision Boundary for Class Imbalanced Learning
Byungju Kim, Junmo Kim
Training of deep neural networks heavily depends on the data distribution. In particular, the networks easily suffer from class imbalance. The trained networks would recognize the…
Learning Not to Learn: Training Deep Neural Networks with Biased Data
Byungju Kim, Hyunwoo Kim, Kyungsu Kim +2
We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased. Since a neural network efficiently learns data distrib…
Mimicking Ensemble Learning with Deep Branched Networks
Byungju Kim, Youngsoo Kim, Yeakang Lee +1
This paper proposes a branched residual network for image classification. It is known that high-level features of deep neural network are more representative than lower-level featu…