  It is common in computer vision to be confronted with domain shift: images
which have the same class but different acquisition conditions. In domain
adaptation (DA), one wants to classify unlabeled target images using source
labeled images. Unfortunately, deep neural networks trained on a source
training set perform poorly on target images which do not belong to the
training domain. One strategy to improve these performances is to align the
source and target image distributions in an embedded space using optimal
transport (OT). However OT can cause negative transfer, i.e. aligning samples
with different labels, which leads to overfitting especially in the presence of
label shift between domains. In this work, we mitigate negative alignment by
explaining it as a noisy label assignment to target images. We then mitigate
its effect by appropriate regularization. We propose to couple the MixUp
regularization \citep{zhang2018mixup} with a loss that is robust to noisy
labels in order to improve domain adaptation performance. We show in an
extensive ablation study that a combination of the two techniques is critical
to achieve improved performance. Finally, we evaluate our method, called
\textsc{mixunbot}, on several benchmarks and real-world DA problems.
