  Unsupervised Domain Adaptation (UDA) aims at reducing the domain gap between
training and testing data and is, in most cases, carried out in offline manner.
However, domain changes may occur continuously and unpredictably during
deployment (e.g. sudden weather changes). In such conditions, deep neural
networks witness dramatic drops in accuracy and offline adaptation may not be
enough to contrast it. In this paper, we tackle Online Domain Adaptation (OnDA)
for semantic segmentation. We design a pipeline that is robust to continuous
domain shifts, either gradual or sudden, and we evaluate it in the case of
rainy and foggy scenarios. Our experiments show that our framework can
effectively adapt to new domains during deployment, while not being affected by
catastrophic forgetting of the previous domains.
