Liu Yali, Zhu Can
School of Art, Jiujiang University, Jiujiang, 332005, China.
College of Media and International Culture, Zhejiang University, Hangzhou, 310058, China.
Sci Rep. 2025 May 6;15(1):15859. doi: 10.1038/s41598-025-00892-9.
In order to explore the application of deep learning (DL) and artificial intelligence (AI) technologies in art education, this work proposes and optimizes an innovative art creation system-Creative Intelligence Cloud (CIC). The system combines a deep generative adversarial network and convolutional neural network, aiming to enhance the automation level, consistency of artistic styles, and creation efficiency in art creation. This work first analyzes existing art creation methods. It points out the shortcomings of traditional systems in terms of image quality, style transfer, and computational performance, especially the application limitations in real teaching scenarios. Therefore, this work designs an art creation model optimized by DL and validates and evaluates it through extensive experiments. The experimental results show that CIC outperforms existing mainstream models in multiple dimensions, including image quality, computational performance, user experience, and style creation. For example, in image quality evaluation, CIC achieves high scores in clarity (0.89), detail performance (0.85), style consistency (0.87), and color accuracy (0.91). In terms of computational performance and resource consumption, CIC shows its superiority, with a training time of only 1500 s, memory consumption of 4.9GB, and a Graphics Processing Unit resource usage rate of 70%. Compared to models such as the Visual Perception Generative Adversarial Network and Artistic Recognition and Transfer Style Convolutional Neural Network, CIC is more efficient and consumes fewer resources. Furthermore, CIC's scores in user experience and style transfer capability are significantly higher than those of other models, providing smoother and more creatively rich art creation tools for art education. Therefore, this work offers new ideas and methods for the application of DL and AI technologies in art creation and art education, and promotes the practical use of AI in art education. The work has certain academic contributions and practical value.
为了探索深度学习(DL)和人工智能(AI)技术在艺术教育中的应用,本文提出并优化了一种创新的艺术创作系统——创意智能云(CIC)。该系统结合了深度生成对抗网络和卷积神经网络,旨在提高艺术创作的自动化水平、艺术风格的一致性以及创作效率。本文首先分析了现有的艺术创作方法。指出了传统系统在图像质量、风格迁移和计算性能方面的缺点,特别是在实际教学场景中的应用局限性。因此,本文设计了一种经DL优化的艺术创作模型,并通过大量实验对其进行验证和评估。实验结果表明,CIC在图像质量、计算性能、用户体验和风格创作等多个维度上均优于现有的主流模型。例如,在图像质量评估中,CIC在清晰度(0.89)、细节表现(0.85)、风格一致性(0.87)和色彩准确性(0.91)方面均取得了高分。在计算性能和资源消耗方面,CIC显示出其优越性,训练时间仅为1500秒,内存消耗为4.9GB,图形处理单元资源使用率为70%。与视觉感知生成对抗网络和艺术识别与迁移风格卷积神经网络等模型相比,CIC效率更高,资源消耗更少。此外,CIC在用户体验和风格迁移能力方面的得分显著高于其他模型,为艺术教育提供了更流畅、更具创意的艺术创作工具。因此,本文为DL和AI技术在艺术创作和艺术教育中的应用提供了新的思路和方法,并推动了AI在艺术教育中的实际应用。该工作具有一定的学术贡献和实用价值。