기계지능 및 데이터사이언스 연구실
M.IN.D Lab (Machine INtelligence and Data science)
지도 문태섭 교수
연구 Machine INtelligence and Data science
TEL 880-8369
WEBSITE https://mindlab-snu.github.io
연구실 소개 및 연구분야
기계지능 및 데이터사이언스 연구실
최근 관심분야 및 주요 연구과제
- 디노이징
- 적응형 기계학습 (연속학습 및 메타학습)
- 설명가능 인공지능
- 공정한 알고리즘
- 멀티모달 데이터 비지도학습
- 뇌과학/의료영상 응용
- 적응형 기계학습 (연속학습 및 메타학습)
- 설명가능 인공지능
- 공정한 알고리즘
- 멀티모달 데이터 비지도학습
- 뇌과학/의료영상 응용
주요 논문/특허
- 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
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
