Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature
  Representations
Jae Young Lee Wonjun Lee Jaehyun Choi Yongkwi Lee Young Seog Yoon
Anomaly detection is a critical and challenging task that aims to identify
data points deviating from normal patterns and distributions within a dataset.
Various methods have been proposed using a one-class-one-model approach, but
these techniques often face practical problems such as memory inefficiency and
the requirement of sufficient data for training. In particular, few-shot
anomaly detection presents significant challenges in industrial applications,
where limited samples are available before mass production. In this paper, we
propose a few-shot anomaly detection method that integrates adversarial
training loss to obtain more robust and generalized feature representations. We
utilize the adversarial loss previously employed in domain adaptation to align
feature distributions between source and target domains, to enhance feature
robustness and generalization in few-shot anomaly detection tasks. We
hypothesize that adversarial loss is effective when applied to features that
should have similar characteristics, such as those from the same layer in a
Siamese network's parallel branches or input-output pairs of
reconstruction-based methods. Experimental results demonstrate that the
proposed method generally achieves better performance when utilizing the
adversarial loss.