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dc.contributor.authorPham, Minh Tuan-
dc.contributor.authorNguyen, An Hung-
dc.contributor.authorHoang, Cao Duy-
dc.descriptionProceeding of The 12th Conference on Information Technology and It's Applications (CITA 2023); pp: 169-180.vi_VN
dc.description.abstractIn the development of deep learning technology, we frequently focus on how to create the best neutral architecture to enhance models and obtain higher accuracy while overlooking a way to speed up training because any parameters are affected by color space. Finding the ideal color space for Quaternion-valued neural network in-depth estimation as survey methods is the focus of this paper. We use a small dataset from the Middlebury dataset [1] to survey training progress in a quaternion-valued neural network that was mentioned in one of our other papers. As a result, we find that HED color-space makes the best training progress in the survey results.vi_VN
dc.publisherVietnam-Korea University of Information and Communication Technologyvi_VN
dc.subjectNeural networksvi_VN
dc.subjectDepth estimationvi_VN
dc.subjectDeep Learningvi_VN
dc.titleEffective Color Spaces for Quaternion-valued Neural Network in Depth Estimationvi_VN
dc.typeWorking Papervi_VN
Appears in Collections:CITA 2023 (National)

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