# Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1302.1700 · HTML: https://mlsanity.com/paper/1302.1700/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2013.

## Built on by (most cited first, 11 of 11)

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- [A Survey on Image Aesthetic Assessment](https://mlsanity.com/paper/2103.11616/) (2021)
- [Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction](https://mlsanity.com/paper/2206.09509/) (2022)
- [Automatic detection of impact craters on Al foils from the Stardust interstellar dust collector using convolutional neural networks](https://mlsanity.com/paper/2103.09673/) (2021)
- [ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer](https://mlsanity.com/paper/2105.02019/) (2021)
- [Leveraging CNNs and Ensemble Learning for Automated Disaster Image Classification](https://mlsanity.com/paper/2311.13531/) (2023)
- [Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays](https://mlsanity.com/paper/2411.12516/) (2024)

## Abstract (arXiv)

Deep Neural Networks now excel at image classification, detection and segmentation. When used to scan images by means of a sliding window, however, their high computational complexity can bring even the most powerful hardware to its knees. We show how dynamic programming can speedup the process by orders of magnitude, even when max-pooling layers are present.