OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
This addresses the challenge of detecting anomalies in images for applications like industrial inspection and medical imaging, offering a novel deep learning approach that is incremental in improving performance on high-dimensional data.
The paper tackled the problem of anomaly detection in high-dimensional images by proposing OIAD, a system based on disentanglement learning that uses only clean samples, achieving over 90% anomaly detection with low false alarm rates across three datasets.
Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as industrial inspection, medical imaging, and security enforcement. There are two key research challenges associated with existing anomaly detection approaches: (1) many approaches perform well on low-dimensional problems however the performance on high-dimensional instances, such as images, is limited; (2) many approaches often rely on traditional supervised approaches and manual engineering of features, while the topic has not been fully explored yet using modern deep learning approaches, even when the well-label samples are limited. In this paper, we propose a One-for-all Image Anomaly Detection system (OIAD) based on disentangled learning using only clean samples. Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency. We implement this idea and evaluate its performance for anomaly detection. Our experiments with three datasets show that OIAD can detect over $90\%$ of anomalies while maintaining a low false alarm rate. It can also detect suspicious samples from samples labeled as clean, coincided with what humans would deem unusual.