We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data. Existing methods focus on minoring the inter-domain gap between the source and target domains. However, the intra-domain knowledge and inherent uncertainty learned by the network are under-explored. In this paper, we propose an orthogonal method, called memory regularization in vivo to exploit the intra-domain knowledge and regularize the model training. Specifically, we refer to the segmentation model itself as the memory module, and minor the discrepancy of the two classifiers, i.e., the primary classifier and the auxiliary classifier, to reduce the prediction inconsistency. Without extra parameters, the proposed method is complementary to the most existing domain adaptation methods and could generally improve the performance of existing methods. Albeit simple, we verify the effectiveness of memory regularization on two synthetic-to-real benchmarks: GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, yielding +11.1% and +11.3% mIoU improvement over the baseline model, respectively. Besides, a similar +12.0% mIoU improvement is observed on the cross-city benchmark: Cityscapes -> Oxford RobotCar.

翻译：我们研究了无监督场景自适应问题，即同时利用有标签的源域数据和无标签的目标域数据进行学习。现有方法主要聚焦于缩小源域与目标域之间的跨域差异，但网络内部学习到的域内知识及其固有不确定性尚未被充分探索。本文提出一种正交方法——记忆体正则化（memory regularization in vivo），用于挖掘域内知识并规范模型训练。具体而言，我们将分割模型本身视为记忆模块，通过最小化两个分类器（即主分类器和辅助分类器）之间的预测差异来降低预测不一致性。该方法无需额外参数，可与现有大多数域自适应方法互补，并普遍提升其性能。尽管形式简洁，我们在两个合成域到真实域的基准测试中验证了记忆体正则化的有效性：GTA5→Cityscapes 和 SYNTHIA→Cityscapes，分别较基线模型提升了11.1%和11.3%的平均交并比（mIoU）。此外，在跨城市基准测试 Cityscapes→Oxford RobotCar 中，也观察到类似的12.0% mIoU提升。