Summary of Colour and Brush Stroke Pattern Recognition in Abstract Art Using Modified Deep Convolutional Generative Adversarial Networks, by Srinitish Srinivasan et al.
Colour and Brush Stroke Pattern Recognition in Abstract Art using Modified Deep Convolutional Generative Adversarial Networks
by Srinitish Srinivasan, Varenya Pathak, Abirami S
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper explores the application of Generative Adversarial Neural Networks (GANs) in studying abstract art. Specifically, it introduces a modified-DCGAN (mDCGAN) designed for high-quality artwork generation, addressing common training pitfalls such as mode collapse and gradient vanishing. The proposed mDCGAN incorporates tailored solutions to the unique demands of art generation, leveraging meticulous adjustments in layer configurations and architectural choices. The approach enables effective study of generated patterns and latent space analysis, demonstrating the potential to revolutionise digital art generation and ecosystem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special kind of computer program called a GAN to create new abstract paintings that are similar to real ones. They wanted to make sure these new paintings look good and are stable, so they made some changes to how the program works. This helped them generate better artwork and understand what makes different brush strokes and colours work together in an abstract painting. The results show that this approach can help us create more realistic digital art and even improve our understanding of art itself. |
Keywords
* Artificial intelligence * Gan * Latent space
