SNIPER: Few-shot Learning for Anomaly Detection to Minimize False-negative Rate with Ensured True-positive Rate

Abstract

In anomaly detection systems, overlooking anomalies may result in serious incidents. Thus, when a system overlooks an anomaly, we need to update the system to never overlook the observed type of anomalies twice. There are roughly two possible approaches to solve this problem; re-training the whole system using all training data, or cascading a new specific detector for the overlooked anomaly. The first approach is the most effective solution; however, a huge computational cost and an amount of anomalous training data are required to re-train the system when it consists of a deep-learning-based anomaly detector. We focused on the latter approach and propose a training method for a cascaded specific anomaly detector using few-shot (just 1 to 3) samples. To suppress the false-negative rate of the overlooked anomaly, the proposed method works to decrease the false-positive rate under the constraint of true-positive rate equaling 1. Experimental results show that the proposed method outperformed conventional cross-entropy-based few-shot learning methods.