{"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/tormentor-deterministic-dynamic-path-data","title":"TorMentor: Deterministic dynamic-path, data augmentations with fractals","arxiv_id":"2204.03776","date":"2022-04-07","proceeding":null,"authors":["Anguelos Nicolaou","Vincent Christlein","Edgar Riba","Jian Shi","Georg Vogeler","Mathias Seuret"],"abstract":"We propose the use of fractals as a means of efficient data augmentation. Specifically, we employ plasma fractals for adapting global image augmentation transformations into continuous local transforms. We formulate the diamond square algorithm as a cascade of simple convolution operations allowing efficient computation of plasma fractals on the GPU. We present the TorMentor image augmentation framework that is totally modular and deterministic across images and point-clouds. All image augmentation operations can be combined through pipelining and random branching to form flow networks of arbitrary width and depth. We demonstrate the efficiency of the proposed approach with experiments on document image segmentation (binarization) with the DIBCO datasets. The proposed approach demonstrates superior performance to traditional image augmentation techniques. Finally, we use extended synthetic binary text images in a self-supervision regiment and outperform the same model when trained with limited data and simple extensions.","url_abs":"https://arxiv.org/abs/2204.03776v1","url_pdf":"https://arxiv.org/pdf/2204.03776v1.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":"tormentor-deterministic-dynamic-path-data","repo_url":"https://github.com/anguelos/tormentor","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"activity-recognition-in-videos","task_name":"Activity Recognition In Videos"},{"task_slug":"binarization","task_name":"Binarization"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"image-augmentation","task_name":"Image Augmentation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"no-real-data-binarization","task_name":"No real Data Binarization"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":null,"task_name":"Synthetic Data Binarization"},{"task_slug":"synthetic-data-generation","task_name":"Synthetic Data Generation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}