  Anomaly detection is crucial in large-scale industrial manufacturing as it
helps detect and localise defective parts. Pre-training feature extractors on
large-scale datasets is a popular approach for this task. Stringent data
security and privacy regulations and high costs and acquisition time hinder the
availability and creation of such large datasets. While recent work in anomaly
detection primarily focuses on the development of new methods built on such
extractors, the importance of the data used for pre-training has not been
studied. Therefore, we evaluated the performance of eight state-of-the-art
methods pre-trained using dynamically generated fractal images on the famous
benchmark datasets MVTec and VisA. In contrast to existing literature, which
predominantly examines the transfer-learning capabilities of fractals, in this
study, we compare models pre-trained with fractal images against those
pre-trained with ImageNet, without subsequent fine-tuning. Although
pre-training with ImageNet remains a clear winner, the results of fractals are
promising considering that the anomaly detection task required features capable
of discerning even minor visual variations. This opens up the possibility for a
new research direction where feature extractors could be trained on
synthetically generated abstract datasets reconciling the ever-increasing
demand for data in machine learning while circumventing privacy and security
concerns.
