{"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/aggregate-decompose-and-fine-tune-a-simple","title":"Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer","arxiv_id":"2311.06749","date":"2023-11-12","proceeding":null,"authors":["Dongping Chen"],"abstract":"Recent advancements have illuminated the efficacy of some tensorization-decomposition Parameter-Efficient Fine-Tuning methods like LoRA and FacT in the context of Vision Transformers (ViT). However, these methods grapple with the challenges of inadequately addressing inner- and cross-layer redundancy. To tackle this issue, we introduce EFfective Factor-Tuning (EFFT), a simple yet effective fine-tuning method. Within the VTAB-1K dataset, our EFFT surpasses all baselines, attaining state-of-the-art performance with a categorical average of 75.9% in top-1 accuracy with only 0.28% of the parameters for full fine-tuning. Considering the simplicity and efficacy of EFFT, it holds the potential to serve as a foundational benchmark. The code and model are now available at https://github.com/Dongping-Chen/EFFT-EFfective-Factor-Tuning.","url_abs":"https://arxiv.org/abs/2311.06749v1","url_pdf":"https://arxiv.org/pdf/2311.06749v1.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":"aggregate-decompose-and-fine-tune-a-simple","repo_url":"https://github.com/dongping-chen/efft-effective-factor-tuning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"aggregate-decompose-and-fine-tune-a-simple","repo_url":"https://github.com/dongping-chen/mixset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"parameter-efficient-fine-tuning","task_name":"parameter-efficient fine-tuning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}