Network Pruning and Fine-tuning for Few-shot Industrial Image Anomaly Detection

This paper focuses on industrial image anomaly detection and localization under few-shot settings. Since acquiring sufficient anomalous data is difficult, unsupervised learning that uses only normal data is commonly used, but even obtaining enough anomaly-free training samples can be challenging. Moreover, applying data augmentations, which is a common strategy for few-shot learning to alleviate the lack of data, is limited to use for some industrial product images. To address the above issues, we propose a network pruning and fine-tuning (PF) framework that leverages the knowledge of a deep pre-trained model. Our approach distills the knowledge of normal samples into a pruned student network, followed by fine-tuning to restore its representation ability for normal data. During inference, discrepancies between features extracted by the teacher and student are used to determine the anomaly score. The proposed method could better utilize the strong representation ability of deep models and benefit the student training with limited data by network pruning. Our framework achieves state-of-the-art performance on the MVTec AD benchmark and is not limited to specific network pruning methods.