# Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies

arXiv: https://arxiv.org/abs/2411.04354 · HTML: https://mlsanity.com/paper/2411.04354/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [Reinforcement Learning in a large scale photonic Recurrent Neural Network](https://mlsanity.com/paper/1711.05133/) (2017)
- [An optical neural network using less than 1 photon per multiplication](https://mlsanity.com/paper/2104.13467/) (2021)
- [Spiking Neural Networks Hardware Implementations and Challenges: a Survey](https://mlsanity.com/paper/2005.01467/) (2020)
- [Understanding and mitigating noise in trained deep neural networks](https://mlsanity.com/paper/2103.07413/) (2021)
- [Fundamental aspects of noise in analog-hardware neural networks](https://mlsanity.com/paper/1907.09002/) (2019)
- [Noise mitigation strategies in physical feedforward neural networks](https://mlsanity.com/paper/2204.09461/) (2022)
- [Data-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multipliers](https://mlsanity.com/paper/2210.09171/) (2022)

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

- [Single Pixel Image Classification using an Ultrafast Digital Light Projector](https://mlsanity.com/paper/2603.12036/) (2026)

## Abstract (arXiv)

In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such networks. In this work, we consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network when applied for specific tasks and including a softmax function in the readout layer. We adapt several noise reduction techniques to the essential setting of classification tasks, which represent a large fraction of neural network computing. We find that these adjusted concepts are highly effective in mitigating the detrimental impact of noise.