Generative adversarial network as a basis for intelligent model of imaging architectural objects based on textual description (Kemerovo, Tomsk)

P. A. Pylov, A. V. Dyagileva, E. A. Nikolaeva, R. V. Maitak, T. A. Shalygina
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Abstract

Вывод: рассматриваемая автоматизирующая система позволит существенно сократить временные, человеческие и денежные ресурсы, требуемые для разработки проекта будущего здания. Перенести в английский вариант Due to the high technology integrated into a person's daily life (smart house), this topic is relevant. One of elements of generative adversarial network is robot vacuum cleaners of various surface. Difficulties caused by this technique largely depend on the environment in which it locates. Purpose: The development of the convolutional neural network concept allowing real-time distinguishing between the building interior and exterior. Practical implication: The proposed intelligent system can distinguish between the building interior and exterior, that will considerably improve the firmware performance of modern technology in both the domestic and industrial segments.
生成对抗网络作为基于文本描述的建筑对象成像智能模型的基础(克麦罗沃,托木斯克)
Вывод: рассматриваемая автоматизирующая система позволит существенно сократить временные, человеческие и денежные ресурсы, требуемые для разработки проекта будущего здания.由于高科技已融入人们的日常生活(智能家居),因此本课题具有现实意义。生成式对抗网络的要素之一是各种表面的机器人吸尘器。这种技术所造成的困难在很大程度上取决于它所处的环境。目的:开发卷积神经网络概念,实时区分建筑物内部和外部。实际意义:建议的智能系统可以区分建筑物内部和外部,这将大大提高现代技术在家庭和工业领域的固件性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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