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Even though the research community has strongly benefited from this, these models showcase characteristics that hinder real-world application. One of the major hurdles is the difficulty in pinpointing what a neural network does not know along with their generalisation capabilities. In this context, the term underspecification has been coined, which describes the generation of different predictors with similar in-domain accuracy but diverging results in OOD. In this paper, we characterise the underspecification distribution and study its connection with epistemic uncertainty. We propose the average-metric epistemic uncertainty that transforms the epistemic uncertainty to the underspecification space. We perform a set of experiments using both LeNet and ResNet18 to solve classification problems on CIFAR-10 and Tiny-ImageNet, respectively. We verify that the average-metric epistemic uncertainty is able to accurately predict, on average, 95% of the predictors that can be obtained from a single architecture. In order to improve the interpretability of neural networks, we suggest utilising the range estimated by the average-metric epistemic uncertainty alongside the accuracy to characterise future state-of-the-art models.<\/jats:p>","DOI":"10.1007\/s00521-025-11415-y","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T17:39:50Z","timestamp":1752255590000},"page":"19579-19595","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Underspecification and uncertainty in deep learning models: Is there a connection?"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4873-8488","authenticated-orcid":false,"given":"Filipa M. 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