# Transfer Learning in Automated Gamma Spectral Identification

arXiv: https://arxiv.org/abs/2003.10524 · HTML: https://mlsanity.com/paper/2003.10524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Sim-to-real supervised domain adaptation for radioisotope identification](https://mlsanity.com/paper/2412.07069/) (2024)
- [Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy](https://mlsanity.com/paper/2603.05719/) (2026)

## Abstract (arXiv)

The models and weights of prior trained Convolutional Neural Networks (CNN) created to perform automated isotopic classification of time-sequenced gamma-ray spectra, were utilized to provide source domain knowledge as training on new domains of potential interest. The previous results were achieved solely using modeled spectral data. In this work we attempt to transfer the knowledge gained to the new, if similar, domain of solely measured data. The ability to train on modeled data and predict on measured data will be crucial in any successful data-driven approach to this problem space.