# LocalNorm: Robust Image Classification through Dynamically Regularized Normalization

arXiv: https://arxiv.org/abs/1902.06550 · HTML: https://mlsanity.com/paper/1902.06550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

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

- [Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition](https://mlsanity.com/paper/2308.16530/) (2023)

## Abstract (arXiv)

While modern convolutional neural networks achieve outstanding accuracy on many image classification tasks, they are, compared to humans, much more sensitive to image degradation. Here, we describe a variant of Batch Normalization, LocalNorm, that regularizes the normalization layer in the spirit of Dropout while dynamically adapting to the local image intensity and contrast at test-time. We show that the resulting deep neural networks are much more resistant to noise-induced image degradation, improving accuracy by up to three times, while achieving the same or slightly better accuracy on non-degraded classical benchmarks. In computational terms, LocalNorm adds negligible training cost and little or no cost at inference time, and can be applied to already-trained networks in a straightforward manner.