# Self-supervised Domain Adaptation for Computer Vision Tasks Source: [Self-supervised Domain Adaptation for Computer Vision Tasks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:58:21.378Z Authors: Jiaolong Xu, Liang Xiao, Antonio M. Lopez Published: 2019-07-25T09:20:29.000Z ## Abstract Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at https://github.com/Jiaolong/self-supervised-da. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multi-source Domain Adaptation on PACS (test)](): Multi-source Domain Adaptation; PACS (test); Accuracy (Art): 88.7. ## Original sources and implementation [Original paper]() [PDF]()