使用CNN模型评估画作的创造力
Using a CNN Model to Assess Paintings' Creativity
AI总结：
本文提出用CNN模型自动评估人类画作创造力，在600幅专业与儿童画作数据集上达到90%准确率且速度优于人工评分，证明了机器学习在该领域的应用潜力。
摘要：
评估艺术创造力长期以来一直困扰着研究人员，传统方法被证明耗时费力。近期研究已将机器学习应用于评估绘画作品中的创造力，但尚未涉及画作。我们的研究通过开发一个CNN模型来自动评估人类画作的创造力，从而填补了这一空白。使用一个包含专业画家和儿童创作的六百幅画作的数据集，我们的模型达到了90%的准确率，且评估时间比人类评分者更快。该方法展示了机器学习在推进艺术创造力评估方面的潜力，为传统方法提供了一种更高效的替代方案。
英文摘要：
Assessing artistic creativity has long challenged researchers, with traditional methods proving time-consuming. Recent studies have applied machine learning to evaluate creativity in drawings, but not paintings. Our research addresses this gap by developing a CNN model to automatically assess the creativity of human paintings. Using a dataset of six hundred paintings by professionals and children, our model achieved 90% accuracy and faster evaluation times than human raters. This approach demonstrates the potential of machine learning in advancing artistic creativity assessment, offering a more efficient alternative to traditional methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
