Summary of A Tiered Gan Approach For Monet-style Image Generation, by Fnu Neha et al.
A Tiered GAN Approach for Monet-Style Image Generation
by FNU Neha, Deepshikha Bhati, Deepak Kumar Shukla, Md Amiruzzaman
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Generative Adversarial Networks (GANs) have been used to generate artistic images that mimic the styles of famous painters like Claude Monet. This paper presents a tiered GAN model that progressively refines image quality through multiple stages, enhancing generated images at each step. The model transforms random noise into detailed artistic representations, addressing common challenges such as training instability, mode collapse, and output quality. By combining downsampling and convolutional techniques, the architecture generates high-quality Monet-style artwork while optimizing computational efficiency. Experimental results demonstrate the ability to produce foundational artistic structures, but further refinements are needed for achieving higher levels of realism and fidelity to Monet’s style. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use Generative Adversarial Networks (GANs) to create art that looks like famous paintings. The goal is to make the generated images look more realistic and detailed, while also being efficient to compute. The approach uses a multi-stage process to refine the image quality, and it can even generate entire artistic structures. While it’s not perfect yet, this method has the potential to create art that looks like Monet paintings. |
Keywords
» Artificial intelligence » Claude » Gan
