REB: Reducing Biases in Representation for Industrial Anomaly Detection
Shuai Lyu Dongmei Mo Waikeung Wong
Existing K-nearest neighbor (KNN) retrieval-based methods usually conduct
industrial anomaly detection in two stages: obtain feature representations with
a pre-trained CNN model and perform distance measures for defect detection.
However, the features are not fully exploited as they ignore domain bias and
the difference of local density in feature space, which limits the detection
performance. In this paper, we propose Reducing Biases (REB) in representation
by considering the domain bias of the pre-trained model and building a
self-supervised learning task for better domain adaption with a defect
generation strategy (DefectMaker) imitating the natural defects. Additionally,
we propose a local density KNN (LDKNN) to reduce the local density bias and
obtain effective anomaly detection. We achieve a promising result of 99.5\%
AUROC on the widely used MVTec AD benchmark. We also achieve 88.0\% AUROC on
the challenging MVTec LOCO AD dataset and bring an improvement of 4.7\% AUROC
to the state-of-the-art result. All results are obtained with smaller backbone
networks such as Vgg11 and Resnet18, which indicates the effectiveness and
efficiency of REB for practical industrial applications.