{"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/attentive-normalization-for-conditional-image","title":"Attentive Normalization for Conditional Image Generation","arxiv_id":"2004.03828","date":"2020-04-08","proceeding":"CVPR 2020 6","authors":["Yi Wang","Ying-Cong Chen","Xiangyu Zhang","Jian Sun","Jiaya Jia"],"abstract":"Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain. It is still not sufficient for categories with complicated structures. In this paper, we characterize long-range dependence with attentive normalization (AN), which is an extension to traditional instance normalization. Specifically, the input feature map is softly divided into several regions based on its internal semantic similarity, which are respectively normalized. It enhances consistency between distant regions with semantic correspondence. Compared with self-attention GAN, our attentive normalization does not need to measure the correlation of all locations, and thus can be directly applied to large-size feature maps without much computational burden. Extensive experiments on class-conditional image generation and semantic inpainting verify the efficacy of our proposed module.","url_abs":"https://arxiv.org/abs/2004.03828v1","url_pdf":"https://arxiv.org/pdf/2004.03828v1.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":"attentive-normalization-for-conditional-image","repo_url":"https://github.com/shepnerd/AttenNorm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"conditional-image-generation","task_name":"Conditional Image Generation"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"semantic-correspondence","task_name":"Semantic correspondence"}],"methods":[{"method_slug":"attentive-normalization","method_name":"Attentive Normalization"},{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.03828","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03828"}},"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/shepnerd/AttenNorm","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":6},"by_repo_kind":{"listed":{"samples":6,"ran":0,"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":0,"samples":[{"code_sha256_prefix":"9ea537af23b53405","entry":"att_normalization","repo":"shepnerd/AttenNorm","repo_kind":"listed","path":"inpaint-attnorm/net/network.py","file_url":"https://github.com/shepnerd/AttenNorm/blob/HEAD/inpaint-attnorm/net/network.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9ea537af23b53405"}},{"code_sha256_prefix":"e6bbab52d3ddea75","entry":"f2uint","repo":"shepnerd/AttenNorm","repo_kind":"listed","path":"inpaint-attnorm/util/util.py","file_url":"https://github.com/shepnerd/AttenNorm/blob/HEAD/inpaint-attnorm/util/util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e6bbab52d3ddea75"}},{"code_sha256_prefix":"d335908a6881e089","entry":"free_form_mask_tf","repo":"shepnerd/AttenNorm","repo_kind":"listed","path":"inpaint-attnorm/net/ops.py","file_url":"https://github.com/shepnerd/AttenNorm/blob/HEAD/inpaint-attnorm/net/ops.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d335908a6881e089"}},{"code_sha256_prefix":"c5bc38ee9334ced5","entry":"gauss_kernel","repo":"shepnerd/AttenNorm","repo_kind":"listed","path":"inpaint-attnorm/net/ops.py","file_url":"https://github.com/shepnerd/AttenNorm/blob/HEAD/inpaint-attnorm/net/ops.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c5bc38ee9334ced5"}},{"code_sha256_prefix":"b1331905adc5865e","entry":"generate_mask_rect","repo":"shepnerd/AttenNorm","repo_kind":"listed","path":"inpaint-attnorm/util/util.py","file_url":"https://github.com/shepnerd/AttenNorm/blob/HEAD/inpaint-attnorm/util/util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b1331905adc5865e"}},{"code_sha256_prefix":"0c044ee49bfaec49","entry":"np_free_form_mask","repo":"shepnerd/AttenNorm","repo_kind":"listed","path":"inpaint-attnorm/net/ops.py","file_url":"https://github.com/shepnerd/AttenNorm/blob/HEAD/inpaint-attnorm/net/ops.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0c044ee49bfaec49"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}