Introduction



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. For more information about our research topics, click here.







Recent Announcements



  • We have a publication accepted for ETRI Journal
    Ji-Hoon Bae, Junho Yim, Nae-Soo Kim, Cheol-Sig Pyo, and Junmo Kim, “Layer-Wise Hint-Based Training for Knowledge Transfer in a Teacher-Student Framework,” ETRI Journal, Vol. 41, No. 2, pp.242-253, April 2019.
    Posted Apr 18, 2019, 1:18 AM by Chanho Lee
  • We have a publication accepted for CVPR 2019.
    Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim and Junmo Kim, "Learning Not to Learn: Training Deep Neural Networks with Biased Data"  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
    Posted Apr 7, 2019, 10:37 PM by Chanho Lee
  • We have a paper accepted for spotlight presentation at NIPS 2018.
    Yunho Jeon and Junmo Kim, "Constructing Fast Network through Deconstruction of Convolution,” Advances in Neural Information Processing Systems (NIPS) 2018.
    Posted Sep 7, 2018, 5:44 AM by 윤주승
  • We have two papers accepted for BMVC 2018.
    1. Byungju Kim, Junho Yim and Junmo Kim, "Highway Driving Dataset for Semantic Video Segmentation"

    2. Donggyu Joo, Junho Yim and Junmo Kim, "Unconstrained Control of Feature Map Size Using Non-integer Strided Sampling"
    Posted Sep 7, 2018, 5:43 AM by 윤주승
  • We have a publication accepted for UAI 2018.
    Sihyeon Seong, Yegang Lee, Youngwook Kee, Dongyoon Han and Junmo Kim, "Towards Flatter Loss Surface via Nonmonotonic Learning Rate Scheduling", The Conference on Uncertainty in Artificial Intelligence (UAI), 2018 - oral paper
    Posted May 18, 2018, 1:55 AM by 윤주승
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