在艺术教育中使用深度学习和基于人工智能的数字技术
Scientific reports
|May 6, 2025
概括
本研究介绍了创意智能云 (CIC),这是一个人工智能和深度学习系统,可以提高艺术创作效率和质量. 在图像质量,风格一致性和艺术教育应用资源消耗方面,CIC的表现优于现有的模型.
科学领域:
- 教育中的人工智能
- 深度学习用于艺术创作
- 计算创造力的创造力
背景情况:
- 传统的艺术创作系统缺乏对现实世界艺术教育的效率和质量.
- 现有的方法在图像质量,风格转移和计算性能方面扎.
- 局限性阻碍了人工智能和深度学习在艺术教学场景中的应用.
研究的目的:
- 提出和优化创新的艺术创作系统,创意智能云 (CIC),利用深度学习和AI.
- 为了提高自动化,艺术风格的一致性,以及艺术生成中的创作效率.
- 解决传统艺术创作方法在教育环境中的缺点.
主要方法:
- 开发了一种通过深度学习 (DL) 优化的艺术创作模型,结合了生成对抗网络和卷积神经网络.
- 通过广泛的实验测试验证并评估了创意智能云 (CIC) 系统.
- 将CIC的表现与主流模型进行比较,例如视觉感知生成对抗网络和艺术识别和转移风格卷积神经网络.
主要成果:
- CIC在图像质量方面表现出卓越的表现 (清晰度0.89,细节0.85,风格一致性0.87,颜色精度0.91).
- 通过1500s的训练时间,4.9GB的内存消耗和70%的GPU使用率,CIC实现了高效率.
- 在用户体验和风格转移能力方面,CIC显著优于其他模型,提供了更丰富的艺术创作工具.
结论:
- 创意智能云 (CIC) 系统为在艺术创作和教育中应用AI和DL提供了一种新的方法.
- CIC为艺术教育提供了一个更高效,更有意识的资源,更具创造性的多功能工具.
- 这项工作在学术和实践上为人工智能在艺术教育和创意实践中的整合做出了贡献.
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