  Anomaly detection in images plays a significant role for many applications
across all industries, such as disease diagnosis in healthcare or quality
assurance in manufacturing. Manual inspection of images, when extended over a
monotonously repetitive period of time is very time consuming and can lead to
anomalies being overlooked.Artificial neural networks have proven themselves
very successful on simple, repetitive tasks, in some cases even outperforming
humans. Therefore, in this paper we investigate different methods of deep
learning, including supervised and unsupervised learning, for anomaly detection
applied to a quality assurance use case. We utilize the MVTec anomaly dataset
and develop three different models, a CNN for supervised anomaly detection,
KD-CAE for autoencoder anomaly detection, NI-CAE for noise induced anomaly
detection and a DCGAN for generating reconstructed images. By experiments, we
found that KD-CAE performs better on the anomaly datasets compared to CNN and
NI-CAE, with NI-CAE performing the best on the Transistor dataset. We also
implemented a DCGAN for the creation of new training data but due to
computational limitation and lack of extrapolating the mechanics of AnoGAN, we
restricted ourselves just to the generation of GAN based images. We conclude
that unsupervised methods are more powerful for anomaly detection in images,
especially in a setting where only a small amount of anomalous data is
available, or the data is unlabeled.
