Summary of Macroscale Fracture Surface Segmentation Via Semi-supervised Learning Considering the Structural Similarity, by Johannes Rosenberger et al.
Macroscale fracture surface segmentation via semi-supervised learning considering the structural similarity
by Johannes Rosenberger, Johannes Tlatlik, Sebastian Münstermann
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 study develops a methodology for semi-supervised training of deep learning models for fracture surface segmentation in macroscopic materials analysis. A weak-to-strong consistency regularization is implemented for semi-supervised learning on three unique datasets created to analyze the influence of structural similarity on segmentation capability. The approach reduces the number of labeled images required for training by a factor of 6 and demonstrates high-quality measurements with mean deviations smaller than 1% using the area average method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a way to teach computers to recognize patterns on broken surfaces without needing a lot of labeled pictures. They made special sets of pictures that are similar but not exactly the same, which helps the computer learn to be good at recognizing things even when they’re slightly different. This is important for materials science because it can help us measure things like how big a crack is just by looking at a picture. |
Keywords
* Artificial intelligence * Deep learning * Regularization * Semi supervised
