# Noise Learning Based Denoising Autoencoder

arXiv: https://arxiv.org/abs/2101.07937 · HTML: https://mlsanity.com/paper/2101.07937/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Generalized Denoising Auto-Encoders as Generative Models](https://mlsanity.com/paper/1305.6663/) (2013)
- [Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues](https://mlsanity.com/paper/1809.08707/) (2018)
- [Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning](https://mlsanity.com/paper/1901.04292/) (2018)
- [When Machine Learning Meets Wireless Cellular Networks: Deployment, Challenges, and Applications](https://mlsanity.com/paper/1911.03585/) (2019)

## Built on by (most cited first, 6 of 6)

- [Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset](https://mlsanity.com/paper/2302.13855/) (2023)
- [Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets](https://mlsanity.com/paper/2205.02096/) (2022)
- [Algorithm for AGC index management against crowded radio environment](https://mlsanity.com/paper/2404.08652/) (2024)
- [Optimizing Predictive Maintenance in Intelligent Manufacturing: An Integrated FNO-DAE-GNN-PPO MDP Framework](https://mlsanity.com/paper/2511.05594/) (2025)
- [AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt](https://mlsanity.com/paper/2512.24625/) (2025)
- [Learning-based Statistical Refinement for Denoising](https://mlsanity.com/paper/2605.04332/) (2026)

## Abstract (arXiv)

This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE.