Summary of Controllable Image Synthesis Of Industrial Data Using Stable Diffusion, by Gabriele Valvano et al.
Controllable Image Synthesis of Industrial Data Using Stable Diffusion
by Gabriele Valvano, Antonino Agostino, Giovanni De Magistris, Antonino Graziano, Giacomo Veneri
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposes a novel approach to reusing pre-trained generative models on small industrial datasets, enabling the generation of self-labelled defective images for defect detection and segmentation. By learning a new concept and conditioning the generative process, the model produces industrial images that satisfy specific topological characteristics and show defects with defined geometry and location. The authors demonstrate the effectiveness of their method by optimizing a crack segmentor for a real-world industrial use case, achieving significant performance improvements when working with limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to recognize defects in industrial products, but you don’t have enough good pictures to train it. Generative AI can help by creating fake images that look like the real ones, but this requires lots of training data too! The researchers came up with a clever way to use pre-trained generative models on small datasets, allowing them to generate pictures of defects for training. They showed that their method works really well when you have limited data, which is important for industrial applications. |
