{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/alphagan-generative-adversarial-networks-for","title":"AlphaGAN: Generative adversarial networks for natural image matting","arxiv_id":"1807.10088","date":"2018-07-26","proceeding":null,"authors":["Sebastian Lutz","Konstantinos Amplianitis","Aljosa Smolic"],"abstract":"We present the first generative adversarial network (GAN) for natural image\nmatting. Our novel generator network is trained to predict visually appealing\nalphas with the addition of the adversarial loss from the discriminator that is\ntrained to classify well-composited images. Further, we improve existing\nencoder-decoder architectures to better deal with the spatial localization\nissues inherited in convolutional neural networks (CNN) by using dilated\nconvolutions to capture global context information without downscaling feature\nmaps and losing spatial information. We present state-of-the-art results on the\nalphamatting online benchmark for the gradient error and give comparable\nresults in others. Our method is particularly well suited for fine structures\nlike hair, which is of great importance in practical matting applications, e.g.\nin film/TV production.","url_abs":"http://arxiv.org/abs/1807.10088v1","url_pdf":"http://arxiv.org/pdf/1807.10088v1.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":"alphagan-generative-adversarial-networks-for","repo_url":"https://github.com/CDOTAD/AlphaGAN-Matting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-matting","task_name":"Image Matting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.10088","atlas_url":"https://app.syntology.ai/?focus=1807.10088","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.10088"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/CDOTAD/AlphaGAN-Matting","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"6a9ca10c725a81cf","entry":"BatchNormGroup","repo":"CDOTAD/AlphaGAN-Matting","repo_kind":"listed","path":"model/AlphaGAN.py","file_url":"https://github.com/CDOTAD/AlphaGAN-Matting/blob/HEAD/model/AlphaGAN.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6a9ca10c725a81cf"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}