Anomaly Detection (AD) is to identify samples that differ from training observations in some way. Those samples that do not follow the distribution of normal data are called outliers or anomalies. In this thesis, we examined two different challenges related to deep learning-based anomaly detection methods. The first challenge is the generalizability to outliers. A wide range of unsupervised anomaly detection methods use deep autoencoders as a foundation. However, a notable limitation of deep autoencoders is that they generalize to outliers and reconstruct them with low error. In order to overcome this issue, we propose an adversarial framework consisting of two competing components, an adversarial distorter, and an autoencoder. During training, the adversarial distorter produces perturbations that are applied to the encoder’s latent space to maximize the reconstruction error. The autoencoder attempts to neutralize the effects of these perturbations to minimize the reconstruction error. Another challenge is the high computational cost, complexity, and unstable training procedures of deep anomaly detection methods. Despite being successful at anomaly detection, deep neural networks are difficult to deploy in real-world applications because of this challenge. We overcome this problem by using a simple learning procedure that trains a lightweight convolutional neural network. We propose to solve anomaly detection as a supervised regression problem. We label normal and anomalous data using two separable distributions of continuous values. As a way to compensate for the lack of anomalous samples during training, we use straightforward image augmentation techniques to create a distinct set of anomalous samples. An augmented set has a distribution that is similar to normal data, but deviates slightly from it, while real anomalies should have a further distribution. Consequently, training a regressor on normal and these augmented samples will result in more distinct distributions of labels for normal and real anomalous data points. In several image and video anomaly detection benchmarks, our methods outperform cutting-edge approaches
