# Deep Neural Networks for Pattern Recognition

arXiv: https://arxiv.org/abs/1809.09645 · HTML: https://mlsanity.com/paper/1809.09645/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 4 of 4)

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- [Neurosymbolic hybrid approach to driver collision warning](https://mlsanity.com/paper/2203.15076/) (2022)
- [Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments](https://mlsanity.com/paper/2107.11043/) (2021)

## Abstract (arXiv)

In the field of pattern recognition research, the method of using deep neural networks based on improved computing hardware recently attracted attention because of their superior accuracy compared to conventional methods. Deep neural networks simulate the human visual system and achieve human equivalent accuracy in image classification, object detection, and segmentation. This chapter introduces the basic structure of deep neural networks that simulate human neural networks. Then we identify the operational processes and applications of conditional generative adversarial networks, which are being actively researched based on the bottom-up and top-down mechanisms, the most important functions of the human visual perception process. Finally, recent developments in training strategies for effective learning of complex deep neural networks are addressed.