{"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/vkd-improving-knowledge-distillation-using","title":"VkD: Improving Knowledge Distillation using Orthogonal Projections","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Roy Miles","Ismail Elezi","Jiankang Deng"],"abstract":"    Knowledge distillation is an effective method for training small and efficient deep learning models. However the efficacy of a single method can degenerate when transferring to other tasks modalities or even other architectures. To address this limitation we propose a novel constrained feature distillation method. This method is derived from a small set of core principles which results in two emerging components: an orthogonal projection and a task-specific normalisation. Equipped with both of these components our transformer models can outperform all previous methods on ImageNet and reach up to a 4.4% relative improvement over the previous state-of-the-art methods. To further demonstrate the generality of our method we apply it to object detection and image generation whereby we obtain consistent and substantial performance improvements over state-of-the-art. Code and models are publicly available.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Miles_VkD_Improving_Knowledge_Distillation_using_Orthogonal_Projections_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Miles_VkD_Improving_Knowledge_Distillation_using_Orthogonal_Projections_CVPR_2024_paper.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":"vkd-improving-knowledge-distillation-using","repo_url":"https://github.com/roymiles/vkd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}