公开(公告)号：US20250111205A1

公开(公告)日：2025-04-03

申请号：US18978437

申请日：2024-12-12

Applicant: Intel Corporation

Inventor： Anthony Daniel Rhodes ,  Celal Savur ,  Bhagyashree Desai ,  Richard Beckwith ,  Giuseppe Raffa

IPC: G06N3/0464 ,  G06N3/08

Abstract: A neural network model for anomaly detection may include convolutional blocks with different spatial scales. The model may be trained with training data, which may be normal data that lacks anomaly. The convolutional blocks may generate embedding features having different spatial scales. A distance between each embedding feature and a corresponding model embedding may be determined. The distances for the embedding features may be accumulated for determining a loss of the model. The model may be trained based on the loss. An accuracy of the trained model may be tested with testing data that has verified anomaly. One or more convolutional blocks may be selected from all the convolutional blocks in the model, e.g., based on the spatial scales of the convolutional blocks and the spatial scale of data on which anomaly detection is to be performed. The selected convolutional block(s) may be used to detect anomaly in the data.