Summary of Paste: Improving the Efficiency Of Visual Anomaly Detection at the Edge, by Manuel Barusco et al.
PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge
by Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze, Francesco Paissan, Elisabetta Farella, Gian Antonio Susto
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 proposed approach to Visual Anomaly Detection (VAD) leverages lightweight neural networks to reduce memory and computation requirements, enabling deployment on resource-constrained edge devices. The method benchmarks major VAD algorithms within this framework and demonstrates feasibility using the MVTec dataset. Additionally, a novel algorithm called Partially Shared Teacher-student (PaSTe) is introduced to address high resource demands of existing approaches like Student Teacher Feature Pyramid Matching (STFPM). PaSTe reduces inference time by 25%, training time by 33%, and peak RAM usage during training by 76%. This improved efficiency makes VAD deployment on edge devices a reality. The proposed method can be applied in various fields such as computer vision, robotics, and artificial intelligence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual Anomaly Detection is a way to find strange pictures and pinpoint where the problem is. This helps without needing lots of labeled data, which takes time and money. But making it work on small devices like phones or cameras hasn’t been easy. Scientists are working on making it faster and using less memory so it can be used in real-life situations. They tested some popular methods and found a new one that works better, called PaSTe. It makes the process faster and uses fewer resources. This is important because it means we can use this technology to help with things like self-driving cars or medical imaging. |
Keywords
» Artificial intelligence » Anomaly detection » Feature pyramid » Inference
