Summary of Crd: Collaborative Representation Distance For Practical Anomaly Detection, by Chao Han and Yudong Yan
CRD: Collaborative Representation Distance for Practical Anomaly Detection
by Chao Han, Yudong Yan
First submitted to arxiv on: 20 Dec 2023
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 proposes an innovative approach to visual defect detection for intelligent industries. The authors develop a collaborative representation model-based method for calculating patch distances, which is more efficient than traditional methods that rely on nearest neighbor searches. This novel solution enables the calculation of distances between patches without accessing the entire stored collection, making it suitable for edge devices with limited computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find a tiny scratch on a pill bottle. Most computers would have trouble doing this quickly and efficiently, especially if they need to compare millions of patches! This paper solves this problem by developing a new way to calculate how similar two patches are without looking at all the other patches first. It’s like having a superpower that lets you find defects in products really fast! |
Keywords
* Artificial intelligence * Nearest neighbor
