使用深度学习方法生成图像

V. Tretynyk, Evgeny Budzinskyi
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引用次数: 0

摘要

简介艺术家总是使用不同的媒介来表达他们的创造力和探索他们的想象力。随着数字技术的发展,艺术家们现在可以使用大量的工具,创作出比以往任何时候都更加精致、复杂和具有视觉吸引力的艺术作品。艺术成像包括应用算法和机器学习(ML)技术来创作数字艺术作品,这些作品可以模仿传统艺术形式的风格、技巧和美学。人工智能系统可以从海量数据中学习,创造出逼真细腻、独特新颖的图像。本文的目的本文采用基于模糊逻辑的方法来估算基辅的住房成本。模糊方法允许对复杂过程进行语言描述,建立概念之间的模糊关系,预测系统行为,创建一组备选行动,正式描述模糊决策规则。文章的目的在这项工作中,使用了一种基于生成模型的方法来生成图像。机器学习方法,即深度神经网络,为解决给定问题提供了广阔的机会。本文探讨了深度学习方法在图像生成中的应用。本文对现有的图像生成方法进行了比较分析。对生成模型提出了修改建议。所开发的系统可生成固定大小(64x64、256x256、1024x1024)的艺术性图像,训练期间 FID 指数的最小值为 128。模型的程序实现采用 Python 编程语言。关键词:卷积神经网络、机器学习、艺术生成、生成竞争网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Deep Learning Methods for Image Generation
Introduction. Artists have always used different mediums to express their creativity and explore their imagination. With the advancement of digital technology, artists now have access to a vast array of tools that allow them to create works of art that are more sophisticated, complex, and visually engaging than ever before. Recently, there has been a growing interest in using artificial intelligence to create images for artistic purposes. Art imaging involves the application of algorithms and machine learning (ML) techniques to create digital works of art that can mimic the styles, techniques, and aesthetics of traditional art forms. Artificial intelligence systems can learn from massive amounts of data to create images that are incredibly realistic and detailed, as well as unique and original. The purpose of the paper. In this paper, an approach based on fuzzy logic was used to estimate the cost of housing in Kyiv. Fuzzy methods allow to apply a linguistic description of complex processes, to establish fuzzy relationships between concepts, to predict the behavior of the system, to create a set of alternative actions, to formally describe fuzzy decision-making rules. The purpose of the article. In this work, an approach based on generative models was used for image generation. Machine learning methods, namely deep neural networks, open wide opportunities for solving the given problem. Results. This paper considers the application of deep learning methods for image generation. A comparative analysis of existing means of image generation was carried out. A proposed modification of the generative model. The developed system generates an image of a fixed size (64x64, 256x256, 1024x1024) of an artistic nature, the minimum value of the FID index during training is 128. The program implementation of the model was performed in the Python programming language. Keywords: convolutional neural networks, machine learning, art generation, generative competitive network.
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