Summary of On Synthetic Texture Datasets: Challenges, Creation, and Curation, by Blaine Hoak and Patrick Mcdaniel
On Synthetic Texture Datasets: Challenges, Creation, and Curation
by Blaine Hoak, Patrick McDaniel
First submitted to arxiv on: 16 Sep 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 The proposed work explores the influence of textures on machine learning models, focusing on texture bias, interpretability, and robustness. The study highlights the limitations of previous works due to the lack of large and diverse texture data. Image generative models are utilized to create high-quality texture images, overcoming challenges in both generation and validation. A novel methodology is introduced for generating diverse texture images, which consists of developing prompts from a range of descriptors, using text-to-image models, and filtering the resulting images. The resulting Prompted Textures Dataset (PTD) contains 362,880 texture images spanning 56 textures. The study also uncovers potential biases in image generation pipelines and highlights unique challenges when working with texture data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how textures affect machine learning models. It’s important to understand this because it can make or break how well a model works. Currently, there isn’t much data available for studying texture bias, so the authors came up with a way to create lots of high-quality texture images using image generative models. They developed a process that uses prompts from different descriptors and filters out low-quality images. The result is a huge dataset called Prompted Textures Dataset (PTD) that can be used for various texture-based tasks. |
Keywords
» Artificial intelligence » Image generation » Machine learning
