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Harmonic Networks: Deep Translation and Rotation Equivariance

Worrall, DE; Garbin, SJ; Turmukhambetov, D; Brostow, GJ; (2017) Harmonic Networks: Deep Translation and Rotation Equivariance. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (pp. pp. 7168-7177). IEEE: Honolulu, HI, USA, USA. Green open access

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Abstract

Translating or rotating an input image should not affect the results of many computer vision tasks. Convolutional neural networks (CNNs) are already translation equivariant: input image translations produce proportionate feature map translations. This is not the case for rotations. Global rotation equivariance is typically sought through data augmentation, but patch-wise equivariance is more difficult. We present Harmonic Networks or H-Nets, a CNN exhibiting equivariance to patch-wise translation and 360-rotation. We achieve this by replacing regular CNN filters with circular harmonics, returning a maximal response and orientation for every receptive field patch. H-Nets use a rich, parameter-efficient and low computational complexity representation, and we show that deep feature maps within the network encode complicated rotational invariants. We demonstrate that our layers are general enough to be used in conjunction with the latest architectures and techniques, such as deep supervision and batch normalization. We also achieve state-of-the-art classification on rotated-MNIST, and competitive results on other benchmark challenges.

Type: Proceedings paper
Title: Harmonic Networks: Deep Translation and Rotation Equivariance
Event: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Location: Honolulu, Hawaii, USA
Dates: 22 July 2017 - 25 July 2017
ISBN-13: 978-1-5386-0457-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CVPR.2017.758
Publisher version: http://dx.doi.org/10.1109/CVPR.2017.758
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Filtering theory, Harmonic analysis, Power harmonic filters, Maximum likelihood detection, Nonlinear filters, Detectors, Computer vision
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10039219
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