Transformer Based Anomaly Detection

Transformer-based anomaly detection leverages the power of self-attention mechanisms to identify unusual patterns in diverse data types, including time series and images, aiming for improved accuracy and efficiency compared to traditional methods. Current research emphasizes enhancing model performance through techniques like dimensionality reduction and incorporating graph convolutional networks to better capture spatial-temporal relationships within data. This approach holds significant promise for various applications, from network intrusion detection and power electronics monitoring to medical image analysis, offering more robust and explainable anomaly detection capabilities. The development of appropriate evaluation metrics is also a key focus to ensure reliable performance assessment.