在人工智能下的艺术创意和动画中培养人才的创业评估系统的分析
1College of fine arts, Sichuan University of Science and Engineering, Zigong, 643000, China.
Scientific reports
|May 15, 2025
概括
本研究介绍了一种人工智能模型,它结合了反向传播神经网络 (BPNN) 和StyleGAN,用于客观的艺术创意评估. 深度学习方法达到96.30%的准确性,为创新和动画教育提供了新的工具.
科学领域:
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
背景情况:
- 传统的艺术创意评估缺乏客观性和定量指标.
- 评估创新和创业精神的主观性阻碍了人才发展.
- 现有的方法很难提供可靠的,数据驱动的洞察力.
研究的目的:
- 利用人工智能和深度学习开发一个客观的艺术创意评估模型.
- 为高等教育动画课程中培养人才提出一种新的方法.
- 将定量分析纳入创新和创业精神的评估中.
主要方法:
- 构建了一个融合模型,集成反向传播神经网络 (BPNN) 和基于风格的生成对抗网络 (StyleGAN).
- 预处理的艺术创意图像数据用于输入BPNN-StyleGAN模型.
- 采用Adam优化器和 Dropout层用于模型训练和通用化,有120次代.
主要成果:
- 开发的模型实现了96.30%的高精度.
- 在准确性,精度,回忆和F1分数等关键指标上,在现有技术上表现出卓越的性能.
- 在损失函数值和合适效果中显示出显著的改善.
结论:
- 由人工智能驱动的模型提供了一个强大的,客观的方法来评估艺术创造力.
- 提供创新的教学工具和方法,以提高学生的实践技能和动画创新.
- 为艺术教育改革和创意产业的人才发展提供了宝贵的见解.
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