Summary of Using a Cnn Model to Assess Paintings’ Creativity, by Zhehan Zhang et al.
Using a CNN Model to Assess Paintings’ Creativity
by Zhehan Zhang, Meihua Qian, Li Luo, Qianyi Gao, Xianyong Wang, Ripon Saha, Xinxin Song
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a novel approach to assessing artistic creativity using machine learning. A convolutional neural network (CNN) model is developed to evaluate the creativity of human paintings, filling a gap in existing studies that have primarily focused on drawings. The proposed method uses a dataset of 600 paintings by professionals and children and achieves an accuracy rate of 90%. This breakthrough demonstrates the potential of machine learning in advancing artistic creativity assessment, offering a more efficient alternative to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how to better measure artistic creativity using computers. Right now, it takes a long time to do this by hand. The scientists developed a new way to use machine learning to evaluate paintings and found that it’s very good at doing this job. They used 600 paintings from professionals and children to train their model, which was able to accurately assess the creativity of these paintings. This is important because it could help us understand more about how people are creative. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Neural network
