PFEAL: A Novel Framework for Image Anomaly Detection Using Pre-Trained Feature Extraction and Activation Learning

Image anomaly detection has emerged as a crucial field in data analysis, pivotal in identifying unusual patterns in vast visualizations, with implications across numerous industries and academic research. Despite its importance, image anomaly detection faces significant challenges, primarily due to the limited availability of anomaly samples that hinder sufficient supervised training. Current methods also suffer from limitations such as reliance on complex network architectures, the necessity of task-specific loss functions, and a tendency for over-generalization, which can compromise detection accuracy. To address these issues, we propose the Pre-trained Feature Extraction for Activation Learning (PFEAL), a novel framework in image anomaly detection that combines a pre-trained feature extractor with an activation learning-based neural network. The PFEAL framework is distinctively designed to operate without relying on complicated model architectures or task-specific loss functions, streamlining the process of anomaly detection. It leverages activation values to reflect the abnormality of input patterns, providing a straightforward, effective, and interpretable anomaly detection framework. Extensive experiments validate the efficacy of PFEAL, demonstrating superior performance compared to baselines and state-of-the-art methods. Moreover, its capabilities for few-shot learning and its intuitive new paradigm offer a flexible and practical approach to image anomaly detection.