# Does deep learning always outperform simple linear regression in optical imaging?

arXiv: https://arxiv.org/abs/1911.00353 · HTML: https://mlsanity.com/paper/1911.00353/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

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

- [Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?](https://mlsanity.com/paper/2204.01637/) (2022)
- [Learning To Help: Training Models to Assist Legacy Devices](https://mlsanity.com/paper/2409.16253/) (2024)
- [Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries](https://mlsanity.com/paper/2409.14575/) (2024)
- [Univariate Radial Basis Function Layers: Brain-inspired Deep Neural Layers for Low-Dimensional Inputs](https://mlsanity.com/paper/2311.16148/) (2023)

## Abstract (arXiv)

Deep learning has been extensively applied in many optical imaging applications in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated. In this work, we show that conventional linear-regression-based methods can outperform the previously proposed deep learning approaches for two black-box optical imaging problems in some extent. Deep learning demonstrates its weakness especially when the number of training samples is small. The advantages and disadvantages of linear-regression-based methods and deep learning are analyzed and compared. Since many optical systems are essentially linear, a deep learning network containing many nonlinearity functions sometimes may not be the most suitable option.