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Most of the common loss functions assume that these images are spatially\naligned and compare pixels at corresponding locations. However, for many tasks,\naligned training pairs of images will not be available. We present an\nalternative loss function that does not require alignment, thus providing an\neffective and simple solution for a new space of problems. Our loss is based on\nboth context and semantics -- it compares regions with similar semantic\nmeaning, while considering the context of the entire image. Hence, for example,\nwhen transferring the style of one face to another, it will translate\neyes-to-eyes and mouth-to-mouth. Our code can be found at\nhttps://www.github.com/roimehrez/contextualLoss","url_abs":"http://arxiv.org/abs/1803.02077v4","url_pdf":"http://arxiv.org/pdf/1803.02077v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-contextual-loss-for-image-transformation","repo_url":"https://github.com/roimehrez/contextualLoss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"the-contextual-loss-for-image-transformation","repo_url":"https://github.com/HilaManor/Generative-deep-features","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"the-contextual-loss-for-image-transformation","repo_url":"https://github.com/S-aiueo32/contextual_loss_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.02077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.02077"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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