Standard
Polynomial Neural Fields for Subband Decomposition and Manipulation. / Yang, Guandao; Benaim, Sagie; Jampani, Varun; Genova, Kyle; Barron, Jonathan T.; Funkhouser, Thomas; Hariharan, Bharath; Belongie, Serge.
Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. red. / S. Koyejo; S. Mohamed; A. Agarwal; D. Belgrave; K. Cho; A. Oh. Neural Information Processing Systems Foundation, 2022. (Advances in Neural Information Processing Systems, Bind 35).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Yang, G, Benaim, S, Jampani, V, Genova, K, Barron, JT, Funkhouser, T, Hariharan, B & Belongie, S 2022, Polynomial Neural Fields for Subband Decomposition and Manipulation. i S Koyejo, S Mohamed, A Agarwal, D Belgrave, K Cho & A Oh (red), Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Neural Information Processing Systems Foundation, Advances in Neural Information Processing Systems, bind 35, 36th Conference on Neural Information Processing Systems, NeurIPS 2022, New Orleans, USA, 28/11/2022.
APA
Yang, G., Benaim, S., Jampani, V., Genova, K., Barron, J. T., Funkhouser, T., Hariharan, B., & Belongie, S. (2022). Polynomial Neural Fields for Subband Decomposition and Manipulation. I S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (red.), Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022 Neural Information Processing Systems Foundation. Advances in Neural Information Processing Systems Bind 35
Vancouver
Yang G, Benaim S, Jampani V, Genova K, Barron JT, Funkhouser T o.a. Polynomial Neural Fields for Subband Decomposition and Manipulation. I Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, red., Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Neural Information Processing Systems Foundation. 2022. (Advances in Neural Information Processing Systems, Bind 35).
Author
Yang, Guandao ; Benaim, Sagie ; Jampani, Varun ; Genova, Kyle ; Barron, Jonathan T. ; Funkhouser, Thomas ; Hariharan, Bharath ; Belongie, Serge. / Polynomial Neural Fields for Subband Decomposition and Manipulation. Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. red. / S. Koyejo ; S. Mohamed ; A. Agarwal ; D. Belgrave ; K. Cho ; A. Oh. Neural Information Processing Systems Foundation, 2022. (Advances in Neural Information Processing Systems, Bind 35).
Bibtex
@inproceedings{e47ccf917d7f4130add4e8e73df98e62,
title = "Polynomial Neural Fields for Subband Decomposition and Manipulation",
abstract = "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.",
author = "Guandao Yang and Sagie Benaim and Varun Jampani and Kyle Genova and Barron, {Jonathan T.} and Thomas Funkhouser and Bharath Hariharan and Serge Belongie",
note = "Publisher Copyright: {\textcopyright} 2022 Neural information processing systems foundation. All rights reserved.; 36th Conference on Neural Information Processing Systems, NeurIPS 2022 ; Conference date: 28-11-2022 Through 09-12-2022",
year = "2022",
language = "English",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural Information Processing Systems Foundation",
editor = "S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh",
booktitle = "Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022",
}
RIS
TY - GEN
T1 - Polynomial Neural Fields for Subband Decomposition and Manipulation
AU - Yang, Guandao
AU - Benaim, Sagie
AU - Jampani, Varun
AU - Genova, Kyle
AU - Barron, Jonathan T.
AU - Funkhouser, Thomas
AU - Hariharan, Bharath
AU - Belongie, Serge
N1 - Publisher Copyright:
© 2022 Neural information processing systems foundation. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
M3 - Article in proceedings
AN - SCOPUS:85152297195
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
A2 - Koyejo, S.
A2 - Mohamed, S.
A2 - Agarwal, A.
A2 - Belgrave, D.
A2 - Cho, K.
A2 - Oh, A.
PB - Neural Information Processing Systems Foundation
T2 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
Y2 - 28 November 2022 through 9 December 2022
ER -
