{"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/visualizing-adapted-knowledge-in-domain","title":"Visualizing Adapted Knowledge in Domain Transfer","arxiv_id":"2104.10602","date":"2021-04-20","proceeding":"CVPR 2021 1","authors":["Yunzhong Hou","Liang Zheng"],"abstract":"A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation process, we portray their knowledge difference with image translation. Specifically, we feed a translated image and its original version to the two models respectively, formulating two branches. Through updating the translated image, we force similar outputs from the two branches. When such requirements are met, differences between the two images can compensate for and hence represent the knowledge difference between models. To enforce similar outputs from the two branches and depict the adapted knowledge, we propose a source-free image translation method that generates source-style images using only target images and the two models. We visualize the adapted knowledge on several datasets with different UDA methods and find that generated images successfully capture the style difference between the two domains. For application, we show that generated images enable further tuning of the target model without accessing source data. Code available at https://github.com/hou-yz/DA_visualization.","url_abs":"https://arxiv.org/abs/2104.10602v2","url_pdf":"https://arxiv.org/pdf/2104.10602v2.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":"visualizing-adapted-knowledge-in-domain","repo_url":"https://github.com/hou-yz/DA_visualization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"explainable-artificial-intelligence","task_name":"Explainable artificial intelligence"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2104.10602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.10602"}},"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/hou-yz/DA_visualization","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/houyz/DA_visualization","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"summary":{"ran":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"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":3,"samples":[{"code_sha256_prefix":"f07ef0b76d0a3a48","entry":"GeneratorResNet","repo":"hou-yz/DA_visualization","repo_kind":"official","path":"SFIT/models/cyclegan.py","file_url":"https://github.com/hou-yz/DA_visualization/blob/HEAD/SFIT/models/cyclegan.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":"f07ef0b76d0a3a48"}},{"code_sha256_prefix":"c63d61ba89207d6d","entry":"ResidualBlock","repo":"hou-yz/DA_visualization","repo_kind":"official","path":"SFIT/models/cyclegan.py","file_url":"https://github.com/hou-yz/DA_visualization/blob/HEAD/SFIT/models/cyclegan.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":"c63d61ba89207d6d"}},{"code_sha256_prefix":"a03a171f3e832cc1","entry":"weights_init_normal","repo":"hou-yz/DA_visualization","repo_kind":"official","path":"SFIT/models/cyclegan.py","file_url":"https://github.com/hou-yz/DA_visualization/blob/HEAD/SFIT/models/cyclegan.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":"a03a171f3e832cc1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}