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However, the bottleneck\nlayer in encoder-decoder usually gives rise to blurry and low quality editing\nresult. And adding skip connections improves image quality at the cost of\nweakened attribute manipulation ability. Moreover, existing methods exploit\ntarget attribute vector to guide the flexible translation to desired target\ndomain. In this work, we suggest to address these issues from selective\ntransfer perspective. Considering that specific editing task is certainly only\nrelated to the changed attributes instead of all target attributes, our model\nselectively takes the difference between target and source attribute vectors as\ninput. Furthermore, selective transfer units are incorporated with\nencoder-decoder to adaptively select and modify encoder feature for enhanced\nattribute editing. Experiments show that our method (i.e., STGAN)\nsimultaneously improves attribute manipulation accuracy as well as perception\nquality, and performs favorably against state-of-the-arts in arbitrary facial\nattribute editing and season translation.","url_abs":"http://arxiv.org/abs/1904.09709v1","url_pdf":"http://arxiv.org/pdf/1904.09709v1.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":"stgan-a-unified-selective-transfer-network","repo_url":"https://github.com/csmliu/STGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"stgan-a-unified-selective-transfer-network","repo_url":"https://github.com/bluestyle97/STGAN-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"stgan-a-unified-selective-transfer-network","repo_url":"https://github.com/2023-MindSpore-4/Code11/tree/main/STGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"stgan-a-unified-selective-transfer-network","repo_url":"https://github.com/2023-MindSpore-4/Code7/tree/main/STGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"stgan-a-unified-selective-transfer-network","repo_url":"https://github.com/JoegameZhou/STGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"stgan-a-unified-selective-transfer-network","repo_url":"https://github.com/Mind23-2/MindCode-85","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":{"status":"ok"}},{"paper_slug":"stgan-a-unified-selective-transfer-network","repo_url":"https://github.com/MindSpore-paper-code-2/code3/tree/main/STGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"stgan-a-unified-selective-transfer-network","repo_url":"https://github.com/mindspore-ai/models/tree/master/research/cv/STGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.09709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09709"}},"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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