XAI methods and test-time augmentation for out-of-domain identification. Wang et al. analysed test-time augmentation as a measure of aleatoric (data-based) uncertainty in the task of image segmentation and found it improved over baseline methods. Later, Ayhan and Berens used test-time augmentation to measure epistemic (model-based) uncertainty in the field of image classification where it again found to improve uncertainty classification. Uncertainty can be useful to identify out-of-domain predictions, but, to our knowledge, test-time augmentation has not yet been applied to that area of uncertainty prediction.