# SSR: SAM is a Strong Regularizer for domain adaptive semantic segmentation Source: [SSR: SAM is a Strong Regularizer for domain adaptive semantic segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:15:27.440Z Authors: Yanqi Ge, Ye Huang, Wen Li, Lixin Duan Published: 2024-01-26T07:16:33.000Z ## Abstract We introduced SSR, which utilizes SAM (segment-anything) as a strong regularizer during training, to greatly enhance the robustness of the image encoder for handling various domains. Specifically, given the fact that SAM is pre-trained with a large number of images over the internet, which cover a diverse variety of domains, the feature encoding extracted by the SAM is obviously less dependent on specific domains when compared to the traditional ImageNet pre-trained image encoder. Meanwhile, the ImageNet pre-trained image encoder is still a mature choice of backbone for the semantic segmentation task, especially when the SAM is category-irrelevant. As a result, our SSR provides a simple yet highly effective design. It uses the ImageNet pre-trained image encoder as the backbone, and the intermediate feature of each stage (ie there are 4 stages in MiT-B5) is regularized by SAM during training. After extensive experimentation on GTA5$\\rightarrow$Cityscapes, our SSR significantly improved performance over the baseline without introducing any extra inference overhead. ## 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. ## Original sources and implementation [Original paper]() [PDF]()