Samara University Repository
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| Title: | Binary convolution model for image classification |
| Keywords: | artificial intelligence binary convolution binary neural networks computer vision computing power convolutional neural networks efficiency of the convolutional process image classification двоичная свертка двоичные нейронные сети вычислительная мощность компьютерное зрение искусственный интеллект классификация изображений эффективность сверточного процесса сверточные нейронные сети |
| Issue Date: | 2024 |
| Citation: | Solis, E. Binary convolution model for image classification / E. Solis, D. Shashev // Информационные технологии и нанотехнологии (ИТНТ-2024) : сб. тр. по материалам X Междунар. конф. и молодеж. шк. (г. Самара, 20-24 мая 2024 г.): в 6 т. / М-во науки и высш. образования Рос. Федерации, Самар. нац. исслед. ун-т им. С. П. Королева (Самар. ун-т). - Самара : Изд-во Самар. ун-та, 2024. - Т. 3: Искусственный интеллект : под ред. А. В. Никонорова, 2024. - С. 030132. |
| Abstract: | Binary Neural Networks present an opportunity for developing Neural Networks that require less computing power as well as energy. This is done through the use of binary values for weights and inputs. This research presents an architecture for image classification where the image and the Convolutional Layers are binarized. The result of the binary convolution is then fed to a non-binary network. |
| URI: | http://repo.ssau.ru/jspui/handle/123456789/12327 |
| Appears in Collections: | Информационные технологии и нанотехнологии |
| File | Size | Format | |
|---|---|---|---|
| 978-5-7883-2080-9_2024-030132.pdf | 281.99 kB | Adobe PDF | View/Open |
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