Polynomial Neural Fields for Subband Decomposition and Manipulation
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Neural fields have emerged as a new paradigm for representing signals, thanks to their ability to do it compactly while being easy to optimize. In most applications, however, neural fields are treated like black boxes, which precludes many signal manipulation tasks. In this paper, we propose a new class of neural fields called polynomial neural fields (PNFs). The key advantage of a PNF is that it can represent a signal as a composition of a number of manipulable and interpretable components without losing the merits of neural fields representation. We develop a general theoretical framework to analyze and design PNFs. We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields. In addition, we empirically demonstrate that Fourier PNFs enable signal manipulation applications such as texture transfer and scale-space interpolation. Code is available at https://github.com/stevenygd/PNF.
Original language | English |
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Title of host publication | Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022 |
Editors | S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh |
Publisher | Neural Information Processing Systems Foundation |
Publication date | 2022 |
ISBN (Electronic) | 9781713871088 |
Publication status | Published - 2022 |
Event | 36th Conference on Neural Information Processing Systems, NeurIPS 2022 - New Orleans, United States Duration: 28 Nov 2022 → 9 Dec 2022 |
Conference
Conference | 36th Conference on Neural Information Processing Systems, NeurIPS 2022 |
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Land | United States |
By | New Orleans |
Periode | 28/11/2022 → 09/12/2022 |
Series | Advances in Neural Information Processing Systems |
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Volume | 35 |
ISSN | 1049-5258 |
Bibliographical note
Publisher Copyright:
© 2022 Neural information processing systems foundation. All rights reserved.
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