  Existing approaches towards anomaly detection~(AD) often rely on a
substantial amount of anomaly-free data to train representation and density
models. However, large anomaly-free datasets may not always be available before
the inference stage; in which case an anomaly detection model must be trained
with only a handful of normal samples, a.k.a. few-shot anomaly detection
(FSAD). In this paper, we propose a novel methodology to address the challenge
of FSAD which incorporates two important techniques. Firstly, we employ a model
pre-trained on a large source dataset to initialize model weights. Secondly, to
ameliorate the covariate shift between source and target domains, we adopt
contrastive training to fine-tune on the few-shot target domain data. To learn
suitable representations for the downstream AD task, we additionally
incorporate cross-instance positive pairs to encourage a tight cluster of the
normal samples, and negative pairs for better separation between normal and
synthesized negative samples. We evaluate few-shot anomaly detection on on 3
controlled AD tasks and 4 real-world AD tasks to demonstrate the effectiveness
of the proposed method.
