{"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/towards-reliable-advertising-image-generation","title":"Towards Reliable Advertising Image Generation Using Human Feedback","arxiv_id":"2408.00418","date":"2024-08-01","proceeding":null,"authors":["Zhenbang Du","Wei Feng","Haohan Wang","Yaoyu Li","Jingsen Wang","Jian Li","Zheng Zhang","Jingjing Lv","Xin Zhu","Junsheng Jin","Junjie Shen","Zhangang Lin","Jingping Shao"],"abstract":"In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention. While generative models automate image generation, they often produce substandard images that may mislead customers and require significant labor costs to inspect. This paper delves into increasing the rate of available generated images. We first introduce a multi-modal Reliable Feedback Network (RFNet) to automatically inspect the generated images. Combining the RFNet into a recurrent process, Recurrent Generation, results in a higher number of available advertising images. To further enhance production efficiency, we fine-tune diffusion models with an innovative Consistent Condition regularization utilizing the feedback from RFNet (RFFT). This results in a remarkable increase in the available rate of generated images, reducing the number of attempts in Recurrent Generation, and providing a highly efficient production process without sacrificing visual appeal. We also construct a Reliable Feedback 1 Million (RF1M) dataset which comprises over one million generated advertising images annotated by human, which helps to train RFNet to accurately assess the availability of generated images and faithfully reflect the human feedback. Generally speaking, our approach offers a reliable solution for advertising image generation.","url_abs":"https://arxiv.org/abs/2408.00418v1","url_pdf":"https://arxiv.org/pdf/2408.00418v1.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":"towards-reliable-advertising-image-generation","repo_url":"https://github.com/ZhenbangDu/Reliable_AD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2408.00418","atlas_url":"https://app.syntology.ai/?focus=2408.00418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.00418"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/ZhenbangDu/Reliable_AD","reach":{"status":"ok"}}],"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":"207f7dfde36360c9","entry":"resize_and_canny","repo":"ZhenbangDu/Reliable_AD","repo_kind":"official","path":"utils/utils.py","file_url":"https://github.com/ZhenbangDu/Reliable_AD/blob/HEAD/utils/utils.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":"207f7dfde36360c9"}},{"code_sha256_prefix":"e14248108fd2691b","entry":"resize_image","repo":"ZhenbangDu/Reliable_AD","repo_kind":"official","path":"utils/utils.py","file_url":"https://github.com/ZhenbangDu/Reliable_AD/blob/HEAD/utils/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e14248108fd2691b"}},{"code_sha256_prefix":"dff8680b72365a59","entry":"choose_scheduler","repo":"ZhenbangDu/Reliable_AD","repo_kind":"official","path":"utils/utils.py","file_url":"https://github.com/ZhenbangDu/Reliable_AD/blob/HEAD/utils/utils.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":"dff8680b72365a59"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}