FILTER PRUNING VIA SOFTMAX ATTENTION
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, S. | - |
dc.contributor.author | Kim, H. | - |
dc.contributor.author | Kwon, Junseok | - |
dc.date.accessioned | 2022-03-17T05:40:04Z | - |
dc.date.available | 2022-03-17T05:40:04Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/55494 | - |
dc.description.abstract | In this paper, we propose a novel network pruning method using the proposed relative depth-wise separable convolutions and softmax attention channel pruning. The relative depth-wise separable convolution enhances conventional depth-wise separable convolutions by enabling the channel interaction, which can prevent accuracy drops even after severe pruning. The softmax attention channel pruning probabilistically expresses the importance of filters and removes unimportant channels efficiently. Experimental results demonstrate that our pruning method outperforms other state-of-the-art pruning methods in terms of Flops, parameters, and top-1 classification accuracy. © 2021 IEEE. | - |
dc.format.extent | 5 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | FILTER PRUNING VIA SOFTMAX ATTENTION | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICIP42928.2021.9506724 | - |
dc.identifier.bibliographicCitation | Proceedings - International Conference on Image Processing, ICIP, v.2021-September, pp 3507 - 3511 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000819455103125 | - |
dc.identifier.scopusid | 2-s2.0-85125570582 | - |
dc.citation.endPage | 3511 | - |
dc.citation.startPage | 3507 | - |
dc.citation.title | Proceedings - International Conference on Image Processing, ICIP | - |
dc.citation.volume | 2021-September | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | Relative depth-wise separable convolutions | - |
dc.subject.keywordAuthor | Softmax attention channel pruning | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scopus | - |
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