1. Bitterwolf, J., Meinke, A., Augustin, M., Hein, M.: Breaking down out-of-distribution detection: many methods based on OOD training data estimate a combination of the same core quantities. In: International Conference on Machine Learning, pp. 2041–2074. PMLR (2022)
2. Cabitza, F., Campagner, A.: The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int. J. Med. Inform. 153, 104510 (2021)
3. DeVries, T., Taylor, G.W.: Learning confidence for out-of-distribution detection in neural networks (2018)
4. Dinh, T.Q., et al.: Performing group difference testing on graph structured data from GANs: analysis and applications in neuroimaging. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 877–889 (2022). https://doi.org/10.1109/TPAMI.2020.3013433
5. Concepts of design assurance for neural networks codann. Standard, European Union Aviation Safety Angency, Daedalean, AG, March 2020. https://www.easa.europa.eu/sites/default/files/dfu/EASA-DDLN-Concepts-of-Design-Assurance-for-Neural-Networks-CoDANN.pdf