Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (1901.08954v1)

Abstract: Despite inherent ill-definition, anomaly detection is a research endeavor 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 a most a subset 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 high-dimensional image space. Furthermore, utilizing an adversarial training scheme for this chosen architecture provides superior reconstruction both within high-dimensional image space and a lower-dimensional latent 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.

An Overview of Skip-GANomaly for Anomaly Detection

The paper "Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection" introduces an innovative unsupervised approach to anomaly detection using a skip-connected encoder-decoder architecture aligned with adversarial training mechanisms. The authors propose this model to address the challenges posed by highly imbalanced datasets in real-world anomaly detection applications, such as X-ray security screening, where anomalous samples are scarce and continually evolving.

The proposed network architecture is a convolutional neural network that incorporates skip connections, which are known to facilitate the retention of both local and global information by allowing direct information transfer across layers. This architectural choice is particularly effective in capturing the multi-scale distribution of normal data within high-dimensional image spaces. Leveraging a Generative Adversarial Network (GAN) framework, the model discriminatively learns the normal data distribution by minimizing the reconstruction error over both image and latent spaces.

The experimental results of Skip-GANomaly are compelling. The model exhibits superior performance across various established benchmarks, including the CIFAR-10 dataset and X-ray image datasets UBA and FFOB specifically curated for anomaly detection. The paper delineates how Skip-GANomaly achieves higher area under the curve (AUC) values compared to state-of-the-art methods like AnoGAN, EGBAD, and GANomaly, showcasing its robustness and efficacy. Specifically, the model achieves up to 0.953 AUC on certain CIFAR-10 classes, and significantly outpaces previous methods on complex real-world X-ray datasets.

The significance of Skip-GANomaly lies in its practical and theoretical contributions to the anomaly detection landscape. Practically, it offers a reproducible and efficient solution for security-critical applications, such as baggage screening, where false negatives can have dire consequences. Theoretically, it provides insights into leveraging skip connections within GANs for multiscale representation learning and anomaly detection. Furthermore, the paper outlines a comprehensive training strategy that combines adversarial, contextual, and latent losses to optimize for realistic and contextually accurate reconstructions in unsupervised settings.

The implications of this research extend into broader areas of AI, especially in the deployment of neural networks in environments characterized by limited annotations of anomalous events. Given the model's flexibility, future developments could explore its application to high-resolution image datasets and scenarios that involve temporal data sequences, such as video surveillance.

Skip-GANomaly exemplifies a promising intersection of adversarial training and encoder-decoder architectures. Its success reveals potential pathways for enhancing anomaly detection models, propelling future research aimed at further refining unsupervised learning methodologies for complex, high-dimensional data distributions.