# Neural Universal Discrete Denoiser

arXiv: https://arxiv.org/abs/1605.07779 · HTML: https://mlsanity.com/paper/1605.07779/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

## Built on by (most cited first, 1 of 1)

- [Supervised Neural Discrete Universal Denoiser for Adaptive Denoising](https://mlsanity.com/paper/2111.12350/) (2021)

## Abstract (arXiv)

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.