Marshall, M. R., Gastelum, Z. N., & Shead, T. M. (2025). Reducing the Feature Distance between Real and Synthetic Data for Training Safeguards-Relevant Computer Vision Models. Journal of the Institute of Nuclear Materials Management, 52(3), 30-42.
ISO-690 (author-date, English)MARSHALL, Matthew R., GASTELUM, Zoe N. und SHEAD, Timothy M., 2025. Reducing the Feature Distance between Real and Synthetic Data for Training Safeguards-Relevant Computer Vision Models. Journal of the Institute of Nuclear Materials Management. 1 Juli 2025. Vol. 52, no. 3, p. 30-42.
Modern Language Association 9th editionMarshall, M. R., Z. N. Gastelum, und T. M. Shead. „Reducing the Feature Distance Between Real and Synthetic Data for Training Safeguards-Relevant Computer Vision Models.“. Journal of the Institute of Nuclear Materials Management, Bd. 52, Nr. 3, Juli 2025, S. 30-42.
Mohr Siebeck - Recht (Deutsch - Österreich)Marshall, Matthew R./Gastelum, Zoe N./Shead, Timothy M.: Reducing the Feature Distance between Real and Synthetic Data for Training Safeguards-Relevant Computer Vision Models., Journal of the Institute of Nuclear Materials Management 2025, 30-42.
Emerald - HarvardMarshall, M.R., Gastelum, Z.N. und Shead, T.M. (2025), „Reducing the Feature Distance between Real and Synthetic Data for Training Safeguards-Relevant Computer Vision Models.“, Journal of the Institute of Nuclear Materials Management, Vol. 52 No. 3, S. 30-42.