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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/60615
Title: Impact of color space on neural networks
Authors: Chen, Y. M.
Tsviatkou, V. Y.
Keywords: материалы конференций;color space;neural network;ResNet;U-Net
Issue Date: 2025
Publisher: БГУИР
Citation: Chen, Y. M. Impact of color space on neural networks / Y. M. Chen, V. Y. Tsviatkou // Технологии передачи и обработки информации : материалы Международного научно-технического семинара, Минск, апрель 2025 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: В. Ю. Цветков [и др.]. – Минск, 2025. – С. 73–76.
Abstract: The choice of color space can significantly affect the performance and interpretability of neural networks in image-based tasks, but its impact remains underexplored in many deep learning applications. This study investigates how different color representations (such as RGB, HSV, LAB, and YCbCr) affect the accuracy and convergence speed of convolutional neural networks (CNNs). Through systematic experiments on benchmark datasets, we evaluate the effectiveness of these color spaces in classification and semantic segmentation tasks. Experimental results show that when using single color space on tasks like classification and semantic segmentation, traditional RGB still hasits advantages.
URI: https://libeldoc.bsuir.by/handle/123456789/60615
Appears in Collections:Технологии передачи и обработки информации : материалы Международного научно-технического семинара (2025)

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