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. 2019 Jul 16;6:162. doi: 10.3389/fmed.2019.00162

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Copyright © 2019 Lafarge, Pluim, Eppenhof and Veta.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Flowchart of the domain-adversarial model. The model is trained using batches balanced across classes or domains. The intermediate representation is learned to optimize the classifier, while the discriminator is trained to identify domains of origin from this representation. Gradient back-propagations are shown with right-to-left arrows, adversarial back-propagation is shown with a sinuous arrow. The domain-invariant representation is illustrated as a selection of three activation maps that are output by the feature extractor, all assigned to RGB channels.