{"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/not-all-models-are-equal-predicting-model","title":"Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space","arxiv_id":"2207.03036","date":"2022-07-07","proceeding":null,"authors":["Wenqi Shao","Xun Zhao","Yixiao Ge","Zhaoyang Zhang","Lei Yang","Xiaogang Wang","Ying Shan","Ping Luo"],"abstract":"This paper addresses an important problem of ranking the pre-trained deep neural networks and screening the most transferable ones for downstream tasks. It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive. Recent advanced methods proposed several lightweight transferability metrics to predict the fine-tuning results. However, these approaches only capture static representations but neglect the fine-tuning dynamics. To this end, this paper proposes a new transferability metric, called \\textbf{S}elf-challenging \\textbf{F}isher \\textbf{D}iscriminant \\textbf{A}nalysis (\\textbf{SFDA}), which has many appealing benefits that existing works do not have. First, SFDA can embed the static features into a Fisher space and refine them for better separability between classes. Second, SFDA uses a self-challenging mechanism to encourage different pre-trained models to differentiate on hard examples. Third, SFDA can easily select multiple pre-trained models for the model ensemble. Extensive experiments on $33$ pre-trained models of $11$ downstream tasks show that SFDA is efficient, effective, and robust when measuring the transferability of pre-trained models. For instance, compared with the state-of-the-art method NLEEP, SFDA demonstrates an average of $59.1$\\% gain while bringing $22.5$x speedup in wall-clock time. The code will be available at \\url{https://github.com/TencentARC/SFDA}.","url_abs":"https://arxiv.org/abs/2207.03036v2","url_pdf":"https://arxiv.org/pdf/2207.03036v2.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":"not-all-models-are-equal-predicting-model","repo_url":"https://github.com/tencentarc/sfda","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"transferability","task_name":"Transferability"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/transferability-on-classification-benchmark","task":"Transferability","dataset":"classification benchmark","model":"SFDA","rank_in_archive_order":2,"of":6,"metrics":{"Kendall's Tau":"0.502"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.03036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.03036"}},"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. 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