Faculty/Research

Research Groups

기계지능 및 데이터사이언스 연구실

M.IN.D Lab (Machine INtelligence and Data science)

Prof. : Prof. Moon, Taesup

Research Area : Machine INtelligence and Data science



  • About the Laboratory & Research Area
  • M.IN.D Lab (Machine INtelligence and Data Science)
  • Research Interests & Projects
  • - Denoising
    - Adaptive Machine Learning (Continual/Incremental/Meta Learning)
    - Explainable AI
    - Algorithmic Fairness
    - Multi-modal data learning
    - Neuroscience applications
  • Journals & Patents
  • - Fair Feature Distillation for Visual Recognition
    Donggyu Lee, Sangwon Jung, Taeeon Park, and Taesup Moon,
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021

    - FBI: Fast Blind Image Denoiser for Source-Dependent Noise (Oral)
    Jaeseok Byun, Sungmin Cha, and Taesup Moon
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021

    - CPR: Classifier-projection regularization for continual learning
    Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flavio P. Calmon, and Taesup Moon
    International Conference on Learning Representations (ICLR), May 2021

    - GAN2GAN: Generative noise learning for blind image denoising with single noisy images
    Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, and Taesup Moon
    International Conference on Learning Representations (ICLR), May 2021

    - Continual Learning with Node-Importance based Adaptive Group Sparse Regularization
    Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon
    Neural Information Processing Systems (NeurIPS), December 2020

    - Learning blind pixelwise affine image denoiser with single noisy images
    Jaeseok Byun and Taesup Moon
    IEEE Signal Processing Letters (IF=3.268), 10.1109/LSP.2020.3002652, June 2020

    - Iterative channel estimation for discrete denoising under channel uncertainty
    Hongjoon Ahn and Taesup Moon
    Conference on Uncertainty in Artificial Intelligence (UAI), August 2020

    - Unsupervised neural universal denoiser for finite-input general-output noisy channel
    Taeeon Park and Taesup Moon
    International Conference on Artificial Intelligence and Statistics (AISTATS), August 2020

    - Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks (press)
    Yongbee Park, Byungjoon Kwon, Juyeon Heo, Xuefei Hu, Yang Liu, and Taesup Moon
    Environmental Pollution (IF=5.714), https://doi.org/10.1016/j.envpol.2019.113395, January 2020

    - Uncertainty-based continual learning with adaptive regularization (code,press)
    Hongjoon Ahn, Sungmin Cha, Donggyu Lee and Taesup Moon
    Proceedings of Neural Information Processing Systems (NeurIPS), December 2019

    - Fooling neural network interpretations via adversarial model manipulation (code,press)
    Juyeon Heo, Sunghwan Joo, and Taesup Moon
    Proceedings of Neural Information Processing Systems (NeurIPS), December 2019

    - Fully convolutional pixel adaptive image denoiser (code)
    Sungmin Cha and Taesup Moon
    Proceedings of IEEE International Conference on Computer Vision (ICCV), October 2019

    - DoPAMINE: Double-sided masked CNN for pixelwise adaptive multiplicative noise despeckling (Oral)
    Sunghwan Joo, Sungmin Cha, and Taesup Moon
    Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), January 2019

    - Neural universal discrete denoiser
    Taesup Moon, Seonwoo Min, Byunghan Lee, and Sungroh Yoon
    Proceedings of Neural Information Processing Systems (NIPS), December 2016

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