{"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/is-synthetic-data-from-diffusion-models-ready","title":"Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?","arxiv_id":"2305.12954","date":"2023-05-22","proceeding":null,"authors":["Zheng Li","YuXuan Li","Penghai Zhao","RenJie Song","Xiang Li","Jian Yang"],"abstract":"Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic images are applicable for knowledge distillation when real images are unavailable. In this paper, we extensively study whether and how synthetic images produced from state-of-the-art diffusion models can be used for knowledge distillation without access to real images, and obtain three key conclusions: (1) synthetic data from diffusion models can easily lead to state-of-the-art performance among existing synthesis-based distillation methods, (2) low-fidelity synthetic images are better teaching materials, and (3) relatively weak classifiers are better teachers. Code is available at https://github.com/zhengli97/DM-KD.","url_abs":"https://arxiv.org/abs/2305.12954v1","url_pdf":"https://arxiv.org/pdf/2305.12954v1.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":"is-synthetic-data-from-diffusion-models-ready","repo_url":"https://github.com/zhengli97/dm-kd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-free-knowledge-distillation","task_name":"Data-free Knowledge Distillation"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"mitigating-contextual-bias","task_name":"Mitigating Contextual Bias"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-learning-on-dtd","task":"Few-Shot Learning","dataset":"DTD","model":"Real-Guidance + CAL","rank_in_archive_order":2,"of":4,"metrics":{"12-shot Accuracy":"54.5","16-shot Accuracy":"57.4","4-shot Accuracy":"41.5","8-shot Accuracy":"50.6"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-learning-on-fgvc-aircraft-1","task":"Few-Shot Learning","dataset":"FGVC Aircraft","model":"Real-Guidance + CAL","rank_in_archive_order":4,"of":4,"metrics":{"12-shot Accuracy":"65.8","16-shot Accuracy":"72.5","4-shot Accuracy":"34.5","8-shot Accuracy":"54.6","Harmonic mean":"34.5"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-learning-on-stanford-cars","task":"Few-Shot Learning","dataset":"Stanford Cars","model":"Real-Guidance + CAL","rank_in_archive_order":2,"of":3,"metrics":{"12-shot Accuracy":"83.9","16-shot Accuracy":"88.3","4-shot Accuracy":"44.3","8-shot Accuracy":"73.1"},"uses_additional_data":false},{"leaderboard":"/sota/mitigating-contextual-bias-on-fgvc-aircraft","task":"Mitigating Contextual Bias","dataset":"FGVC Aircraft","model":"CAL + Real-Guidance","rank_in_archive_order":3,"of":4,"metrics":{"OOD Accuracy (%)":"17.7","Top-1 Accuracy (%)":"71.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.12954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12954"}},"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/zhengli97/dm-kd","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"824d70be2701bb63","entry":"get_teacher_name","repo":"zhengli97/dm-kd","repo_kind":"official","path":"train_student.py","file_url":"https://github.com/zhengli97/dm-kd/blob/HEAD/train_student.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"824d70be2701bb63"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}