Summary of Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task, by Jie Liu et al.
Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
by Jie Liu, Yao Wu, Xiaotong Luo, Zongze Wu
First submitted to arxiv on: 9 Jun 2024
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 A novel research direction is proposed in this paper, focusing on anomaly multi-classification, which aims to identify anomalous items and classify their type. To tackle the challenges of applying few-shot learning to this task, a baseline model combining RelationNet and PatchCore is introduced. A data generation method is also proposed, creating pseudo classes and a proxy task to bridge the gap between training data and industrial scenarios. Contrastive learning is utilized to improve the vanilla baseline, achieving better performance than directly fine-tuning a ResNet. Experimental results on MvTec AD and MvTec3D AD demonstrate the superiority of this approach in anomaly multi-classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists are trying to solve a new problem where they not only find strange things but also figure out what kind of thing it is. They’re using special computer models and ways to create fake training data to help their computers learn how to do this job better. The results show that their approach works really well for identifying different kinds of unusual things. |
Keywords
» Artificial intelligence » Classification » Few shot » Fine tuning » Resnet
