{"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/on-instabilities-of-deep-learning-in-image","title":"On instabilities of deep learning in image reconstruction - Does AI come at a cost?","arxiv_id":"1902.05300","date":"2019-02-14","proceeding":null,"authors":["Vegard Antun","Francesco Renna","Clarice Poon","Ben Adcock","Anders C. Hansen"],"abstract":"Deep learning, due to its unprecedented success in tasks such as image\nclassification, has emerged as a new tool in image reconstruction with\npotential to change the field. In this paper we demonstrate a crucial\nphenomenon: deep learning typically yields unstablemethods for image\nreconstruction. The instabilities usually occur in several forms: (1) tiny,\nalmost undetectable perturbations, both in the image and sampling domain, may\nresult in severe artefacts in the reconstruction, (2) a small structural\nchange, for example a tumour, may not be captured in the reconstructed image\nand (3) (a counterintuitive type of instability) more samples may yield poorer\nperformance. Our new stability test with algorithms and easy to use software\ndetects the instability phenomena. The test is aimed at researchers to test\ntheir networks for instabilities and for government agencies, such as the Food\nand Drug Administration (FDA), to secure safe use of deep learning methods.","url_abs":"http://arxiv.org/abs/1902.05300v1","url_pdf":"http://arxiv.org/pdf/1902.05300v1.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":"on-instabilities-of-deep-learning-in-image","repo_url":"https://github.com/vegarant/Invfool","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1902.05300","atlas_url":"https://app.syntology.ai/?focus=1902.05300","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}