{"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/hot-refresh-model-upgrades-with-regression-1","title":"Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval","arxiv_id":"2201.09724","date":"2022-01-24","proceeding":null,"authors":["Binjie Zhang","Yixiao Ge","Yantao Shen","Yu Li","Chun Yuan","Xuyuan Xu","Yexin Wang","Ying Shan"],"abstract":"The task of hot-refresh model upgrades of image retrieval systems plays an essential role in the industry but has never been investigated in academia before. Conventional cold-refresh model upgrades can only deploy new models after the gallery is overall backfilled, taking weeks or even months for massive data. In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly. Compatible training has made it possible, however, the problem of model regression with negative flips poses a great challenge to the stable improvement of user experience. We argue that it is mainly due to the fact that new-to-old positive query-gallery pairs may show less similarity than new-to-new negative pairs. To solve the problem, we introduce a Regression-Alleviating Compatible Training (RACT) method to properly constrain the feature compatibility while reducing negative flips. The core is to encourage the new-to-old positive pairs to be more similar than both the new-to-old negative pairs and the new-to-new negative pairs. An efficient uncertainty-based backfilling strategy is further introduced to fasten accuracy improvements. Extensive experiments on large-scale retrieval benchmarks (e.g., Google Landmark) demonstrate that our RACT effectively alleviates the model regression for one more step towards seamless model upgrades. The code will be available at https://github.com/binjiezhang/RACT_ICLR2022.","url_abs":"https://arxiv.org/abs/2201.09724v1","url_pdf":"https://arxiv.org/pdf/2201.09724v1.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":"hot-refresh-model-upgrades-with-regression-1","repo_url":"https://github.com/binjiezhang/RACT_ICLR2022","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.09724","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.09724"}},"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":"deterministic:regex_extraction","url":"https://github.com/binjiezhang/RACT_ICLR2022","reach":null}],"summary":{"ran":1,"ran_fixture":1,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"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":4,"samples":[{"code_sha256_prefix":"7e88236232ed3219","entry":"BackwardCompatibleLoss","repo":"binjiezhang/RACT_ICLR2022","repo_kind":"official","path":"model/loss.py","file_url":"https://github.com/binjiezhang/RACT_ICLR2022/blob/HEAD/model/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"7e88236232ed3219"}},{"code_sha256_prefix":"5374b1e2fb86a416","entry":"calculate_loss","repo":"binjiezhang/RACT_ICLR2022","repo_kind":"official","path":"model/loss.py","file_url":"https://github.com/binjiezhang/RACT_ICLR2022/blob/HEAD/model/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"5374b1e2fb86a416"}},{"code_sha256_prefix":"06851d0b47833458","entry":"euclidean_dist","repo":"binjiezhang/RACT_ICLR2022","repo_kind":"official","path":"model/loss.py","file_url":"https://github.com/binjiezhang/RACT_ICLR2022/blob/HEAD/model/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"06851d0b47833458"}},{"code_sha256_prefix":"e67543831f7c035e","entry":"gather_tensor","repo":"binjiezhang/RACT_ICLR2022","repo_kind":"official","path":"model/loss.py","file_url":"https://github.com/binjiezhang/RACT_ICLR2022/blob/HEAD/model/loss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"e67543831f7c035e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}