{"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/trainable-fractional-fourier-transform","title":"Trainable Fractional Fourier Transform","arxiv_id":null,"date":"2024-03-04","proceeding":"IEEE Signal Processing Letters 2024 3","authors":["Emirhan Koç","Tuna Alikaşifoğlu","Arda Can Aras","Aykut Koç"],"abstract":"Recently, the fractional Fourier transform (FrFT) has been integrated into distinct deep neural network (DNN) models such as transformers, sequence models, and convolutional neural networks (CNNs). In these studies, the fraction order is considered a hyperparameter and tuned manually to find the suitable values. By taking these one step further, we extend the scope of FrFT and introduce it as a trainable layer in various neural network architectures, where the fraction order is learned in the training stage along with the network weights. First, we mathematically show that fraction order can be updated through backpropagation in the network training phase. To support this formulation, we conduct extensive experiments encompassing image classification and time series prediction tasks on benchmark datasets. Our results show that the trainable FrFT layers alleviate the need to search for suitable fraction orders and improve performance over time and Fourier domain approaches.","url_abs":"https://ieeexplore.ieee.org/document/10458263","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10458263","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":"trainable-fractional-fourier-transform","repo_url":"https://github.com/tunakasif/torch-frft","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"trainable-fractional-fourier-transform","repo_url":"https://github.com/koc-lab/TrainableFrFT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}