{"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/deeper-image-quality-transfer-training-low","title":"Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images","arxiv_id":"1808.05577","date":"2018-08-16","proceeding":null,"authors":["Stefano B. Blumberg","Ryutaro Tanno","Iasonas Kokkinos","Daniel C. Alexander"],"abstract":"In this paper we address the memory demands that come with the processing of\n3-dimensional, high-resolution, multi-channeled medical images in deep\nlearning. We exploit memory-efficient backpropagation techniques, to reduce the\nmemory complexity of network training from being linear in the network's depth,\nto being roughly constant $ - $ permitting us to elongate deep architectures\nwith negligible memory increase. We evaluate our methodology in the paradigm of\nImage Quality Transfer, whilst noting its potential application to various\ntasks that use deep learning. We study the impact of depth on accuracy and show\nthat deeper models have more predictive power, which may exploit larger\ntraining sets. We obtain substantially better results than the previous\nstate-of-the-art model with a slight memory increase, reducing the\nroot-mean-squared-error by $ 13\\% $. Our code is publicly available.","url_abs":"http://arxiv.org/abs/1808.05577v1","url_pdf":"http://arxiv.org/pdf/1808.05577v1.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":"deeper-image-quality-transfer-training-low","repo_url":"https://github.com/sbb-gh/Deeper-Image-Quality-Transfer-Training-Low-Memory-Neural-Networks-for-3D-Images","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.05577","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}