OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
- URL: http://arxiv.org/abs/2001.06640v2
- Date: Thu, 26 Mar 2020 09:00:14 GMT
- Title: OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
- Authors: Shuo Wang, Tianle Chen, Shangyu Chen, Carsten Rudolph, Surya Nepal,
Marthie Grobler
- Abstract summary: We propose a One-for-all Image Anomaly Detection system based on disentangled learning using only clean samples.
Our experiments with three datasets show that OIAD can detect over $90%$ of anomalies while maintaining a low false alarm rate.
- Score: 23.48763375455514
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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