Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection

This study investigates the performance of robust anomaly detection models in industrial inspection, focusing particularly on their ability to handle noisy data. We propose to leverage the adaptation ability of meta-learning approaches to identify and reject noisy training data to improve the learning process. In our model, we employ Model Agnostic Meta-Learning (MAML) and an iterative refinement process through an Inter-Quartile Range rejection scheme to enhance their adaptability and robustness. This approach significantly improves the models' capability to distinguish between normal and defective conditions. Our results of experiments conducted on well-known MVTec and KSDD2 datasets demonstrate that the proposed method not only excels in environments with substantial noise but can also contribute in case of a clear training set, isolating those samples that are relatively out of distribution, thus offering significant improvements over traditional models.