PUAD: Frustratingly Simple Method for Robust Anomaly Detection
Shota Sugawara Ryuji Imamura
Developing an accurate and fast anomaly detection model is an important task
in real-time computer vision applications. There has been much research to
develop a single model that detects either structural or logical anomalies,
which are inherently distinct. The majority of the existing approaches
implicitly assume that the anomaly can be represented by identifying the
anomalous location. However, we argue that logical anomalies, such as the wrong
number of objects, can not be well-represented by the spatial feature maps and
require an alternative approach. In addition, we focused on the possibility of
detecting logical anomalies by using an out-of-distribution detection approach
on the feature space, which aggregates the spatial information of the feature
map. As a demonstration, we propose a method that incorporates a simple
out-of-distribution detection method on the feature space against
state-of-the-art reconstruction-based approaches. Despite the simplicity of our
proposal, our method PUAD (Picturable and Unpicturable Anomaly Detection)
achieves state-of-the-art performance on the MVTec LOCO AD dataset.