{"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/diffeomorphic-temporal-alignment-nets","title":"Diffeomorphic Temporal Alignment Nets","arxiv_id":null,"date":"2019-12-10","proceeding":"NeurIPS 2019 12","authors":["Ron Shapira Weber","Matan Eyal","Nicki Skafte","Oren Shriki","Oren Freifeld"],"abstract":"Time-series analysis is confounded by nonlinear time warping of the data. Traditional methods for joint alignment do not generalize: after aligning a given signal ensemble, they lack a mechanism, that does not require solving a new optimization problem, to align previously-unseen signals. In the multi-class case, they must also first classify the test data before aligning it. Here we propose the Diffeomorphic Temporal alignment Net (DTAN), a learning-based method for time-series joint alignment. Via flexible temporal transformer layers, DTAN learns and applies an input-dependent nonlinear time warping to its input signal. Once learned, DTAN easily aligns previously-unseen signals by its inexpensive forward pass. In a single-class case, the method is unsupervised: the ground-truth alignments are unknown. In the multi-class case, it is semi-supervised in the sense that class labels (but not the ground-truth alignments) are used during learning; in test time, however, the class labels are unknown. As we show, DTAN not only outperforms existing joint-alignment methods in aligning training data but also generalizes well to test data. Our code is available at https://github.com/BGU-CS-VIL/dtan.","url_abs":"https://neurips.cc/Conferences/2019/Schedule?showEvent=13767","url_pdf":"https://www.cs.bgu.ac.il/~orenfr/DTAN/ShapiraWeber_NeurIPS_2019.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":"diffeomorphic-temporal-alignment-nets","repo_url":"https://github.com/BGU-CS-VIL/dtan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"ecg-classification","task_name":"ECG Classification"},{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-alignment","task_name":"Time Series Alignment"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-averaging","task_name":"Time Series Averaging"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}