TADA: Taxonomy Adaptive Domain Adaptation

• URL: http://arxiv.org/abs/2109.04813v1
• Date: Fri, 10 Sep 2021 11:58:56 GMT
• Title: TADA: Taxonomy Adaptive Domain Adaptation
• Authors: Rui Gong, Martin Danelljan, Dengxin Dai, Wenguan Wang, Danda Pani Paudel, Ajad Chhatkuli, Fisher Yu, Luc Van Gool
• Abstract summary: Traditional domain adaptation addresses the task of adapting a model to a novel target domain under limited supervision. We introduce the more general taxonomy adaptive domain adaptation problem, allowing for inconsistent between the two domains. On the label-level, we employ a bilateral mixed sampling strategy to augment the target domain, and a relabelling method to unify and align the label spaces.
• Score: 143.68890984935726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional domain adaptation addresses the task of adapting a model to a novel target domain under limited or no additional supervision. While tackling the input domain gap, the standard domain adaptation settings assume no domain change in the output space. In semantic prediction tasks, different datasets are often labeled according to different semantic taxonomies. In many real-world settings, the target domain task requires a different taxonomy than the one imposed by the source domain. We therefore introduce the more general taxonomy adaptive domain adaptation (TADA) problem, allowing for inconsistent taxonomies between the two domains. We further propose an approach that jointly addresses the image-level and label-level domain adaptation. On the label-level, we employ a bilateral mixed sampling strategy to augment the target domain, and a relabelling method to unify and align the label spaces. We address the image-level domain gap by proposing an uncertainty-rectified contrastive learning method, leading to more domain-invariant and class discriminative features. We extensively evaluate the effectiveness of our framework under different TADA settings: open taxonomy, coarse-to-fine taxonomy, and partially-overlapping taxonomy. Our framework outperforms previous state-of-the-art by a large margin, while capable of adapting to new target domain taxonomies.

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