Statistical Inference &
Information Theory Laboratory
The Statistical Inference and Information Theory Laboratory is directed by professor Junmo Kim since 2009. Our research focuses on development of theoretical methods which can be applied to image processing, computer vision, pattern recognition, and machine learning.
Research Topics
We mainly focus on developing deep learning algorithms to solve many problems in the computer vision field. Including general tasks in computer vision such as classification or detection, we also study applicable work like generative models(GAN), depth estimation, medical imaging, noisy data, semantic segmentation, etc. Besides, there are many research on the fundamental structure of neural networks such as network minimization and architecture search in our laboratory. Our various research topics and details can be found in the Research and Publication section.
Deep Learning Applications
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).
Neural Network Architecture
Our research on neural network architectures aims to find the optimal structure for convolutional neural networks. Recent topics include designing new architectures, or new operations in the network, and automated architecture search.
Learning Methods
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.
News [More]
[New] We have a publication accepted for CVPR 2020.
"Regularization on Spatio-Temporally Smoothed Feature for Action Recognition", Jinhyung Kim, Seunghwan Cha, Dongyoon Wee, Soonmin Bae, and Junmo Kim
[New] We have a publication accepted for CVPR 2020.
"Continual Learning with Extended Kronecker-factored Approximate Curvature", Janghyeon Lee, Hyeong Gwon Hong, Donggyu Joo, and Junmo Kim
[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