Our research topics in application of deep learning are focused on autonomous driving (depth estimation, object tracking, multi-sensor recognition), video recognition (video Turing test, video captioning), and defect detection (manufacturing, medical imaging).
We study various topics in learning methods to solve problems in data used for training, such as noisy labels or biases. We also aim to improve utilization of neural networks in real life, by knowledge distillation, network pruning, and continual learning.
[New] We have a publication accepted for AAAI 2020.
"Residual Continual Learning", Janghyeon Lee, Donggyu Joo, Hyeong Gwon Hong, and Junmo Kim
We have a publication accepted for ICCV 2019.
"NLNL: Negative Learning for Noisy Labels", Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim
We have a publication accepted for CVPR 2019.
"Learning Not to Learn: Training Deep Neural Networks with Biased Data", Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim and Junmo Kim