Summary of Resad: a Simple Framework For Class Generalizable Anomaly Detection, by Xincheng Yao and Zixin Chen and Chao Gao and Guangtao Zhai and Chongyang Zhang
ResAD: A Simple Framework for Class Generalizable Anomaly Detection
by Xincheng Yao, Zixin Chen, Chao Gao, Guangtao Zhai, Chongyang Zhang
First submitted to arxiv on: 26 Oct 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 A unified anomaly detection (AD) model that can generalize to detect anomalies in diverse classes from different domains without retraining or fine-tuning is proposed. The existing one-for-one AD models are poorly class-generalizable due to significant variations in normal feature representations across classes, causing performance drops dramatically when used for new classes. ResAD, a simple yet effective framework, learns the residual feature distribution instead of the initial feature distribution, reducing feature variations and enabling direct adaptation to new classes. ResAD consists of three components: Feature Converter, Feature Constraintor, and Feature Distribution Estimator. Despite its simplicity, ResAD achieves remarkable anomaly detection results when directly used in new classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection is a way to find unusual patterns in data. This paper tries to make it easier to detect these anomalies by creating one model that can work on different types of data without needing to learn each type separately. They found that current models don’t work well because they’re designed for specific types of data and don’t handle changes well. To fix this, they created a new model called ResAD that focuses on the differences between normal and abnormal patterns rather than trying to understand every single pattern. This helps the model adapt better to new situations and detect anomalies more effectively. |
Keywords
» Artificial intelligence » Anomaly detection » Fine tuning
