# Learning to Detect with Constant False Alarm Rate

arXiv: https://arxiv.org/abs/2206.05747 · HTML: https://mlsanity.com/paper/2206.05747/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 12 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Learning Techniques for Inverse Problems in Imaging](https://mlsanity.com/paper/2005.06001/) (2020)
- [On Calibration and Out-of-domain Generalization](https://mlsanity.com/paper/2102.10395/) (2021)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Achieving Equalized Odds by Resampling Sensitive Attributes](https://mlsanity.com/paper/2006.04292/) (2020)
- [Learning to Estimate Without Bias](https://mlsanity.com/paper/2110.12403/) (2021)
- [SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network](https://mlsanity.com/paper/2106.05490/) (2021)
- [Machine Learning For In-Region Location Verification In Wireless Networks](https://mlsanity.com/paper/1812.07853/) (2018)

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

- [Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication](https://mlsanity.com/paper/2210.12494/) (2022)

## Abstract (arXiv)

We consider the use of machine learning for hypothesis testing with an emphasis on target detection. Classical model-based solutions rely on comparing likelihoods. These are sensitive to imperfect models and are often computationally expensive. In contrast, data-driven machine learning is often more robust and yields classifiers with fixed computational complexity. Learned detectors usually provide high accuracy with low complexity but do not have a constant false alarm rate (CFAR) as required in many applications. To close this gap, we propose to add a term to the loss function that promotes similar distributions of the detector under any null hypothesis scenario. Experiments show that our approach leads to near CFAR detectors with similar accuracy as their competitors.