Domain Adaptation for Semantic Segmentation With Maximum Squares Loss
Deep neural networks for semantic segmentation always require a large number of samples with pixel level labels, which becomes the major difficulty in their real world applications. To reduce the labeling cost, unsupervised domain adaptation (UDA) approaches are proposed to transfer knowledge from labeled synthesized datasets to unlabeled real world datasets. Recently, some semi supervised learning methods have been applied to UDA and achieved state of the art performance. One of the most popular approaches in semi supervised learning is the entropy minimization method. However, when applying the entropy minimization to UDA for semantic segmentation, the gradient of the entropy is biased towards samples that are easy to transfer. To balance the gradient of well classified target samples, we propose the maximum squares loss. Our maximum squares loss prevents the training process being dominated by easy to transfer samples in the target domain. Besides, we introduce the image wise weighting ratio to alleviate the class imbalance in the unlabeled target domain. Both synthetic to real and cross city adaptation experiments demonstrate the effectiveness of our proposed approach. The code is released at https:/github. com/ZJULearning/MaxSquareLoss.
2019 IEEE/CVF International Conference on Computer Vision (ICCV)