A5101352535- Anomaly Detection Techniques and Applications
- Fault Detection and Control Systems
- Image and Video Stabilization
- Network Security and Intrusion Detection
- Face and Expression Recognition
- Industrial Vision Systems and Defect Detection
- Advanced Sensor and Control Systems
- Face recognition and analysis
Zhejiang Lab
2024
In industrial contexts, anomaly detection is crucial for ensuring quality control and maintaining operational efficiency in manufacturing processes. Leveraging high-level features extracted from ImageNet-trained networks the robust capabilities of Deep Support Vector Data Description (SVDD) model detection, this paper proposes an improved SVDD model, termed Feature-Patching (FPSVDD), designed unsupervised applications. This integrates a feature-patching technique with framework. Features are...
The task of anomaly detection is to separate anomalous data from normal in the dataset. Models such as deep convolutional autoencoder (CAE) network and supporting vector description (SVDD) model have been universally employed demonstrated significant success detecting anomalies. However, over-reconstruction ability CAE for can easily lead high false negative rate data. On other hand, SVDD has drawback feature collapse, which leads a decrease accuracy To address these problems, we propose...
The application of facial expression recognition technology has been widely used in fields such as medicine, education, and the internet. Extracting features is a critical step recognition, representational ability their adaptability to classifiers directly impact final results. We propose feature extraction optimization method based on conditional generative adversarial neural networks, applied conjunction with scalable weakly supervised clustering (WSC) approach for classification. Unlike...