Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

By Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon. IEEE International Joint Conference on Neural Network, 2019

461 citations recorded by the source corpus.

Despite inherent ill-definition, anomaly detection is a research endeavour of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data distribution based on some measure of normality. The most significant challenge in real-world anomaly detection problems is that available data is highly imbalanced towards normality (i.e. non-anomalous) and contains at most a sub-set of all possible anomalous samples - hence limiting the use of well-established supervised learning methods. By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain, and hence detect abnormality based on deviation from this model. Our proposed approach employs an encoder-decoder convolutional neural network with skip connections to thoroughly capture the multi-scale distribution of the normal data distribution in image space. Furthermore, utilizing an adversarial training scheme for this chosen architecture provides superior reconstruction both within image space and a lower-dimensional embedding vector space encoding. Minimizing the reconstruction error metric within both the image and hidden vector spaces during training aids the model to learn the distribution of normality as required. Higher reconstruction metrics during subsequent test and deployment are thus indicative of a deviation from this normal distribution, hence indicative of an anomaly. Experimentation over established anomaly detection benchmarks and challenging real-world datasets, within the context of X-ray security screening, shows the unique promise of such a proposed approach.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5435a9ab36a308cef10bc725104e8f778ed3a328

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Citations in the corpus

DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection (2021, methodology): “Reconstructive methods, such as Autoencoders [5, 1, 2, 26] and GANs [24, 23], have been extensively explored since they enable learning of a powerful reconstruction sub-space, using only anomaly-free images.”
ADBench: Anomaly Detection Benchmark (2022, background): “For instance, some semi-supervised models are trained only on normal samples, and detect anomalies that deviate from the normal representations learned in the training process [7, 8, 188].”
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization (2020, unknown): “At the image level, the simplest approach is to take the reconstructed error as an anomaly score [10] but additional information from the latent space [16], [18], intermediate activations [19] or a discriminator [17], [20] can help to better recognize anomalous images.”