b'@online{Gong2109.04813,'b'\nTITLE = {{TADA}: {T}axonomy Adaptive Domain Adaptation},\nAUTHOR = {Gong, Rui and Danelljan, Martin and Dai, Dengxin and Wang, Wenguan and Paudel, Danda Pani and Chhatkuli, Ajad and Yu, Fisher and Van Gool, Luc},\nLANGUAGE = {eng},\nURL = {https://arxiv.org/abs/2109.04813},\nEPRINT = {2109.04813},\nEPRINTTYPE = {arXiv},\nYEAR = {2021},\nABSTRACT = {Traditional domain adaptation addresses the task of adapting a model to a<br>novel target domain under limited or no additional supervision. While tackling<br>the input domain gap, the standard domain adaptation settings assume no domain<br>change in the output space. In semantic prediction tasks, different datasets<br>are often labeled according to different semantic taxonomies. In many<br>real-world settings, the target domain task requires a different taxonomy than<br>the one imposed by the source domain. We therefore introduce the more general<br>taxonomy adaptive domain adaptation (TADA) problem, allowing for inconsistent<br>taxonomies between the two domains. We further propose an approach that jointly<br>addresses the image-level and label-level domain adaptation. On the<br>label-level, we employ a bilateral mixed sampling strategy to augment the<br>target domain, and a relabelling method to unify and align the label spaces. We<br>address the image-level domain gap by proposing an uncertainty-rectified<br>contrastive learning method, leading to more domain-invariant and class<br>discriminative features. We extensively evaluate the effectiveness of our<br>framework under different TADA settings: open taxonomy, coarse-to-fine<br>taxonomy, and partially-overlapping taxonomy. Our framework outperforms<br>previous state-of-the-art by a large margin, while capable of adapting to<br>target taxonomies.<br>},\n}\n'