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In this context, Unsupervised Domain Adaptation (UDA) can transfer knowledge from unlimited synthetic annotations to unlabeled real images of a given domain. UDA methods are composed of an initial training stage with labeled synthetic data followed by a second stage for feature alignment between labeled synthetic and unlabeled real data. In this paper, we propose a novel approach for UDA focusing the initial training stage, which leads to increased performance after adaptation. We introduce a curriculum strategy where each semantic class is learned progressively. Thereby, better features are obtained for the second stage. This curriculum is based on: (1) a class-scoring function to determine the difficulty of each semantic class, (2) a strategy for incremental learning based on scoring and pacing functions that limits the required training time unlike standard curriculum-based training and (3) a training loss to operate at class level. We extensively evaluate our approach as the first stage of several state-of-the-art UDA methods for semantic segmentation. Our results demonstrate significant performance enhancements across all methods: improvements of up to 10% for entropy-based techniques and 8% for adversarial methods. These findings underscore the dependency of UDA on the accuracy of the initial training. The implementation is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/vpulab\/PCCL\" ext-link-type=\"uri\">https:\/\/github.com\/vpulab\/PCCL<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s00371-024-03373-8","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T18:02:01Z","timestamp":1713204121000},"page":"901-919","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Per-class curriculum for Unsupervised Domain Adaptation in semantic segmentation"],"prefix":"10.1007","volume":"41","author":[{"given":"Roberto","family":"Alcover-Couso","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan C.","family":"SanMiguel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcos","family":"Escudero-Vi\u00f1olo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo","family":"Carballeira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"3373_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-023-03056-w","author":"Q Feng","year":"2023","unstructured":"Feng, Q., Li, F., Li, H., Liu, X., Fei, J., Xu, S., Lu, C., Yang, Q.: Feature reused network: a fast segmentation network model for strip steel surfaces defects based on feature reused. 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