{"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/exploring-patch-wise-semantic-relation-for","title":"Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks","arxiv_id":"2203.01532","date":"2022-03-03","proceeding":"CVPR 2022 1","authors":["Chanyong Jung","Gihyun Kwon","Jong Chul Ye"],"abstract":"Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence. However, the methods often ignore the diverse semantic relation within the images. To address this, here we propose a novel semantic relation consistency (SRC) regularization along with the decoupled contrastive learning, which utilize the diverse semantics by focusing on the heterogeneous semantics between the image patches of a single image. To further improve the performance, we present a hard negative mining by exploiting the semantic relation. We verified our method for three tasks: single-modal and multi-modal image translations, and GAN compression task for image translation. Experimental results confirmed the state-of-art performance of our method in all the three tasks.","url_abs":"https://arxiv.org/abs/2203.01532v1","url_pdf":"https://arxiv.org/pdf/2203.01532v1.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":"exploring-patch-wise-semantic-relation-for","repo_url":"https://github.com/jcy132/Hneg_SRC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.01532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01532"}},"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/jcy132/Hneg_SRC","reach":null}],"summary":{"ran":2},"by_repo_kind":{"official":{"samples":2,"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":0,"samples":[{"code_sha256_prefix":"c48872b4c38c1e7a","entry":"Normalize","repo":"jcy132/Hneg_SRC","repo_kind":"official","path":"Single-modal/models/SRC.py","file_url":"https://github.com/jcy132/Hneg_SRC/blob/HEAD/Single-modal/models/SRC.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c48872b4c38c1e7a"}},{"code_sha256_prefix":"47c3f1c6cb133ee6","entry":"SRC_Loss","repo":"jcy132/Hneg_SRC","repo_kind":"official","path":"Single-modal/models/SRC.py","file_url":"https://github.com/jcy132/Hneg_SRC/blob/HEAD/Single-modal/models/SRC.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"47c3f1c6cb133ee6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}