PFAD: Parameter-Efficient Framework for Cross-Domain Anomaly Detection for Sustainable Manufacturing

Deploying visual anomaly detection in industrial production requires retraining models for each product domain, leading to substantial costs in data collection, computational resources, and energy consumption that scale poorly across diverse manufacturing environments. This paper proposes PFAD, a parameter-efficient framework for cross-domain anomaly detection without retraining, enabling the direct deployment of a source model trained on a benchmark dataset to unseen industrial settings in a zero-shot manner. PFAD leverages a frozen vision transformer backbone and introduces Soft Anomaly-Aware Feature Selection (Soft AFS), which assigns continuous weights to feature channels based on anomaly discriminability, preserving information while enhancing cross-domain generalization without relying on synthetic anomalies or target-domain data. Extensive experiments on both public benchmarks and real-world industrial datasets demonstrate that PFAD achieves strong cross-domain performance, including an image-level AUROC of 0.945 for semiconductor PCB inspection using only a public dataset for training. Furthermore, PFAD supports an optional one-shot inference extension, where a single normal reference image improves detection performance in scenarios with large domain gaps (up to +10.4 pp), most effectively when zero-shot transfer leaves meaningful headroom. These results demonstrate that PFAD provides a practical and scalable solution for industrial anomaly detection by eliminating repeated retraining cycles and reducing associated computational and energy overhead, while maintaining high performance across heterogeneous domains.