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Poster
Neural Universal Discrete Denoiser
Taesup Moon · Seonwoo Min · Byunghan Lee · Sungroh Yoon

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #108
in Tuesday Posters »

We present a new framework of applying deep neural networks (DNN) to devise a universal discrete denoiser. Unlike other approaches that utilize supervised learning for denoising, we do not require any additional training data. In such setting, while the ground-truth label, i.e., the clean data, is not available, we devise ``pseudo-labels'' and a novel objective function such that DNN can be trained in a same way as supervised learning to become a discrete denoiser. We experimentally show that our resulting algorithm, dubbed as Neural DUDE, significantly outperforms the previous state-of-the-art in several applications with a systematic rule of choosing the hyperparameter, which is an attractive feature in practice.

Keywords: Deep Learning or Neural Networks · Information Theory · (Other) Unsupervised Learning Methods · (Application) Signal and Speech Processing

Author Information

Taesup Moon (DGIST)

Taesup Moon is currently an associate professor at Seoul National University (SNU), Korea. Prior to joining SNU in 2021, he was an associate professor at Sungkyunkwan University (SKKU) from 2017 to 2021, an assistant professor at DGIST from 2015 to 2017, a research staff member at Samsung Advanced Institute of Technology (SAIT) from 2013 to 2015, a postdoctoral researcher at UC Berkeley, Statistics from 2012 to 2013, and a research scientist at Yahoo! Labs from 2008 to 2012. He got his Ph.D. and MS degrees in Electrical Engineering from Stanford University, CA USA in 2008 and 2004, respectively, and his BS degree in Electrical Engineering from Seoul National University, Korea in 2002. His research interests are in deep learning, statistical machine learning, data science, signal processing, and information theory.

Seonwoo Min (Seoul National University)
Byunghan Lee (Seoul National University)
Sungroh Yoon (Seoul National University)

Dr. Sungroh Yoon is Associate Professor of Electrical and Computer Engineering at Seoul National University, Korea. Prof. Yoon received the B.S. degree from Seoul National University, South Korea, and the M.S. and Ph.D. degrees from Stanford University, CA, respectively, all in electrical engineering. He held research positions with Stanford University, CA, Intel Corporation, Santa Clara, CA, and Synopsys, Inc., Mountain View, CA. He was an Assistant Professor with the School of Electrical Engineering, Korea University, from 2007 to 2012. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, Seoul National University, South Korea. Prof. Yoon is the recipient of 2013 IEEE/IEIE Joint Award for Young IT Engineers. His research interests include deep learning, machine learning, data-driven artificial intelligence, and large-scale applications including biomedicine.

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