Towards Ensuring Software Interoperability Between Deep Learning Frameworks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Youn Kyu | - |
dc.contributor.author | Park, Seong Hee | - |
dc.contributor.author | Lim, Min Young | - |
dc.contributor.author | Lee, Soo-Hyun | - |
dc.contributor.author | Jeong, Jongwook | - |
dc.date.accessioned | 2023-12-11T07:31:31Z | - |
dc.date.available | 2023-12-11T07:31:31Z | - |
dc.date.issued | 2023-10-01 | - |
dc.identifier.issn | 2083-2567 | - |
dc.identifier.issn | 2449-6499 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32116 | - |
dc.description.abstract | With the widespread of systems incorporating multiple deep learning models, ensuring interoperability between target models has become essential. However, due to the unreliable performance of existing model conversion solutions, it is still challenging to ensure interoperability between the models developed on different deep learning frameworks. In this paper, we propose a systematic method for verifying interoperability between pre- and post-conversion deep learning models based on the validation and verification approach. Our proposed method ensures interoperability by conducting a series of systematic verifications from multiple perspectives. The case study confirmed that our method successfully discovered the interoperability issues that have been reported in deep learning model conversions. | - |
dc.format.extent | 14 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SCIENDO | - |
dc.title | Towards Ensuring Software Interoperability Between Deep Learning Frameworks | - |
dc.type | Article | - |
dc.publisher.location | 폴란드 | - |
dc.identifier.doi | 10.2478/jaiscr-2023-0016 | - |
dc.identifier.scopusid | 2-s2.0-85176784826 | - |
dc.identifier.wosid | 001094732400001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH, v.13, no.4, pp 215 - 228 | - |
dc.citation.title | JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH | - |
dc.citation.volume | 13 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 215 | - |
dc.citation.endPage | 228 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | SOLAR-RADIATION | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | interoperability | - |
dc.subject.keywordAuthor | validation&verification | - |
dc.subject.keywordAuthor | deep learning frameworks | - |
dc.subject.keywordAuthor | model conversion | - |
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