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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18152

Title: A Novel Filter Pruning Algorithm for Vision Tasks based on Kernel Grouping
Authors: Lee, Jongmin
Elibol, Armagan
Nak-Young, Chong
Issue Date: 2022-07
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2022 19th International Conference on Ubiquitous Robots (UR)
Start page: 213
End page: 218
DOI: 10.1109/UR55393.2022.9826290
Abstract: Although the size and the computation cost of the state of the art deep learning models are tremendously large, they run without any problem when implemented on computers thanks to the remarkable enhancements and advancements of computers. However, the problem is likely to be faced when the need for deploying them on mobile platforms arises. Model compression techniques such as filter pruning or knowledge distillation help to reduce the size of deep learning models. However the conventional methods contain sorting algorithms therefore they cannot be applied to models that have reshaping layers like involution. In this research, we revisit a model compression algorithm named Model Diet that can be both applied to involution and convolution models. Furthermore, we present its application on two different tasks, image segmentation and depth estimation.
Rights: This is the author's version of the work. Copyright (C)2022 IEEE. 2022 19th International Conference on Ubiquitous Robots (UR), 2022, pp.213-218. DOI: 10.1109/UR55393.2022.9826290. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/18152
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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