{"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/diff-sysc-an-approach-using-diffusion-models","title":"Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification","arxiv_id":null,"date":"2025-02-25","proceeding":"ICAART 2025 2","authors":["Paul-Dumitru Orasan","Alexandra-Ioana Albu","Gabriela Czibula"],"abstract":"Diffusion models have revolutionized the field of generative machine learning due to their effectiveness in capturing complex, multimodal data distributions. Semi-supervised learning represents a technique that allows the extraction of information from a large corpus of unlabeled data, assuming that a small subset of labeled data is provided. While many generative methods have been previously used in semi-supervised learning tasks, only few approaches have integrated diffusion models in such a context. In this work, we are adapting state-of-the-art generative diffusion models to the problem of semi-supervised image classification. We propose Diff-SySC, a new semi supervised, pseudo-labeling pipeline which uses a diffusion model to learn the conditional probability distribution characterizing the label generation process. Experimental evaluations highlight the robustness of Diff-SySC when evaluated on image classification benchmarks and show that it outperforms related work approaches on CIFAR-10 and STL-10, while achieving competitive performance on CIFAR-100. Overall, our proposed method outperforms the related work in 90.74% of the cases.","url_abs":"https://www.scitepress.org/PublicationsDetail.aspx?ID=8MzrzMi7kMs%3d&t=1","url_pdf":"https://www.scitepress.org/Papers/2025/130971/130971.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":[],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"semi-supervised-image-classification","task_name":"Semi-Supervised Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-6","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10, 250 Labels","model":"Diff-SySC","rank_in_archive_order":4,"of":27,"metrics":{"Percentage error":"3.65±0.10"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar","task":"Semi-Supervised Image Classification","dataset":"CIFAR-10, 4000 Labels","model":"Diff-SySC","rank_in_archive_order":3,"of":49,"metrics":{"Percentage error":"3.26±0.06"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-stl-1","task":"Semi-Supervised Image Classification","dataset":"STL-10, 1000 Labels","model":"Diff-SySC","rank_in_archive_order":1,"of":13,"metrics":{"Accuracy":"99.36±0.20"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}