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Learning on the Edge: Explicit Boundary Handling in CNNs

Innamorati, C; Ritschel, T; Weyrich, T; Mitra, N; (2018) Learning on the Edge: Explicit Boundary Handling in CNNs. Proceedings of the British Machine Vision Conference (BMVC) Green open access

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Abstract

Convolutional neural networks (CNNs) handle the case where filters extend beyond the image boundary using several heuristics, such as zero, repeat or mean padding. These schemes are applied in an ad-hoc fashion and, being weakly related to the image content and oblivious of the target task, result in low output quality at the boundary. In this paper, we propose a simple and effective improvement that learns the boundary handling itself. At training-time, the network is provided with a separate set of explicit boundary filters. At testing-time, we use these filters which have learned to extrapolate features at the boundary in an optimal way for the specific task. Our extensive evaluation, over a wide range of architectural changes (variations of layers, feature channels, or both), shows how the explicit filters result in improved boundary handling. Consequently, we demonstrate an improvement of 5 % to 20 % across the board of typical CNN applications (colorization, de-Bayering, optical flow, and disparity estimation)

Type: Article
Title: Learning on the Edge: Explicit Boundary Handling in CNNs
Open access status: An open access version is available from UCL Discovery
Publisher version: http://bmvc2018.org/contents/papers/0471.pdf
Language: English
Additional information: © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
UCL classification: 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/10051915
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