# Noise robust neural network architecture

arXiv: https://arxiv.org/abs/2305.09276 · HTML: https://mlsanity.com/paper/2305.09276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 22 of 22)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Feature Denoising for Improving Adversarial Robustness](https://mlsanity.com/paper/1812.03411/) (2018)
- [Adversarial Examples Improve Image Recognition](https://mlsanity.com/paper/1911.09665/) (2019)
- [On Detecting Adversarial Perturbations](https://mlsanity.com/paper/1702.04267/) (2017)
- [A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions](https://mlsanity.com/paper/1705.02498/) (2017)
- [Comparing deep neural networks against humans: object recognition when the signal gets weaker](https://mlsanity.com/paper/1706.06969/) (2017)
- [Humans can decipher adversarial images](https://mlsanity.com/paper/1809.04120/) (2018)
- [On Classification of Distorted Images with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.01924/) (2017)
- [Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets](https://mlsanity.com/paper/1509.03413/) (2015)
- [Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy](https://mlsanity.com/paper/1704.04650/) (2017)
- [Random Quantum Neural Networks (RQNN) for Noisy Image Recognition](https://mlsanity.com/paper/2203.01764/) (2022)
- [Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models](https://mlsanity.com/paper/2205.07372/) (2022)
- [Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters](https://mlsanity.com/paper/1806.08037/) (2018)

## Abstract (arXiv)

In which we propose neural network architecture (dune neural network) for recognizing general noisy image without adding any artificial noise in the training data. By representing each free parameter of the network as an uncertainty interval, and applying a linear transformation to each input element, we show that the resulting architecture achieves decent noise robustness when faced with input data with white noise. We apply simple dune neural networks for MNIST dataset and demonstrate that even for very noisy input images which are hard for human to recognize, our approach achieved better test set accuracy than human without dataset augmentation. We also find that our method is robust for many other examples with various background patterns added.