  Vision-based inspection algorithms have significantly contributed to quality
control in industrial settings, particularly in addressing structural defects
like dent and contamination which are prevalent in mass production. Extensive
research efforts have led to the development of related benchmarks such as
MVTec AD (Bergmann et al., 2019). However, in industrial settings, there can be
instances of logical defects, where acceptable items are found in unsuitable
locations or product pairs do not match as expected. Recent methods tackling
logical defects effectively employ knowledge distillation to generate
difference maps. Knowledge distillation (KD) is used to learn normal data
distribution in unsupervised manner. Despite their effectiveness, these methods
often overlook the potential false negatives. Excessive similarity between the
teacher network and student network can hinder the generation of a suitable
difference map for logical anomaly detection. This technical report provides
insights on handling potential false negatives by utilizing a simple constraint
in KD-based logical anomaly detection methods. We select EfficientAD as a
state-of-the-art baseline and apply a margin-based constraint to its
unsupervised learning scheme. Applying this constraint, we can improve the
AUROC for MVTec LOCO AD by 1.3 %.
