Chapter 3 Anomaly Detection with Variational Autoencoders In this chapter, we present a method for the detection of surface defects in images of 3D-printed products, which enables automated visual quality control. The data characterising this problem is typically high-dimensional (high-resolution images), imbalanced (defects are relatively rare), and has few labelled examples. We approach these challenges by formulating the problem as probabilistic anomaly detection, where we use a Variational Autoencoder (VAE) to estimate the probability density of non-faulty products. We train the VAE in an unsupervised manner on images of non-faulty products only. A successful model will then assign high likelihood to unseen images of non-faulty products, and lower likelihood to images displaying defects. We test this method on anomaly detection scenarios using the MNIST dataset, as well as on images of 3D-printed products. The demonstrated performance is related to the capability of the model to closely estimate the density distribution of the non-faulty data. For both datasets we present empirical results that the likelihood estimated with a convolutional VAE can separate the normal and anomalous data. Moreover we show how the reconstruction capabilities of VAEs are highly informative for human observers towards localising potential anomalies, which can aid the quality control process. The contents of this chapter are largely based on our paper Anomaly Detection for Visual Quality Control of 3D-Printed Products (Tonnaer et al., 2019) and includes additional results from our paper Anomaly detection for imbalanced datasets with deep generative models (Santos Buitrago et al., 2018).
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