{"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/cross-domain-adversarial-auto-encoder","title":"Cross-Domain Adversarial Auto-Encoder","arxiv_id":"1804.06078","date":"2018-04-17","proceeding":null,"authors":["Haodi Hou","Jing Huo","Yang Gao"],"abstract":"In this paper, we propose the Cross-Domain Adversarial Auto-Encoder (CDAAE)\nto address the problem of cross-domain image inference, generation and\ntransformation. We make the assumption that images from different domains share\nthe same latent code space for content, while having separate latent code space\nfor style. The proposed framework can map cross-domain data to a latent code\nvector consisting of a content part and a style part. The latent code vector is\nmatched with a prior distribution so that we can generate meaningful samples\nfrom any part of the prior space. Consequently, given a sample of one domain,\nour framework can generate various samples of the other domain with the same\ncontent of the input. This makes the proposed framework different from the\ncurrent work of cross-domain transformation. Besides, the proposed framework\ncan be trained with both labeled and unlabeled data, which makes it also\nsuitable for domain adaptation. Experimental results on data sets SVHN, MNIST\nand CASIA show the proposed framework achieved visually appealing performance\nfor image generation task. Besides, we also demonstrate the proposed method\nachieved superior results for domain adaptation. Code of our experiments is\navailable in https://github.com/luckycallor/CDAAE.","url_abs":"http://arxiv.org/abs/1804.06078v1","url_pdf":"http://arxiv.org/pdf/1804.06078v1.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":"cross-domain-adversarial-auto-encoder","repo_url":"https://github.com/luckycallor/CDAAE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.06078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06078"}},"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. 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/luckycallor/CDAAE","reach":null}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"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":2,"samples":[{"code_sha256_prefix":"7f82f1ecdbaa0296","entry":"load_data","repo":"luckycallor/CDAAE","repo_kind":"official","path":"cdaae.py","file_url":"https://github.com/luckycallor/CDAAE/blob/HEAD/cdaae.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7f82f1ecdbaa0296"}},{"code_sha256_prefix":"57b0d095a061e876","entry":"merge_images","repo":"luckycallor/CDAAE","repo_kind":"official","path":"cdaae.py","file_url":"https://github.com/luckycallor/CDAAE/blob/HEAD/cdaae.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"57b0d095a061e876"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}