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]
"Enhanced Prototypical Learning for Unsupervised Domain Adaptation in LiDAR Semantic Segmentation", Eojindl Yi, Juyoung Yang and Junmo Kim
"Joint Negative and Positive Learning for Noisy Labels", Youngdong Kim, Juseung Yun, Hyounguk Shon and Junmo Kim