# Enhancing CNNs robustness to occlusions with bioinspired filters for border completion

arXiv: https://arxiv.org/abs/2504.17619 · HTML: https://mlsanity.com/paper/2504.17619/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Normative theory of visual receptive fields](https://mlsanity.com/paper/1701.06333/) (2017)
- [Leveraging dendritic properties to advance machine learning and neuro-inspired computing](https://mlsanity.com/paper/2306.08007/) (2023)
- [Random wiring, ganglion cell mosaics, and the functional architecture of the visual cortex](https://mlsanity.com/paper/1510.00332/) (2015)

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

- [Bioinspired CNNs for border completion in occluded images](https://mlsanity.com/paper/2603.10694/) (2026)

## Abstract (arXiv)

We exploit the mathematical modeling of the visual cortex mechanism for border completion to define custom filters for CNNs. We see a consistent improvement in performance, particularly in accuracy, when our modified LeNet 5 is tested with occluded MNIST images.