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Autoencoder-based Anomaly Detection
Khoa Anh Ngo(노안콰),Junhan Kim(김준한),Jiseob Kim(김지섭),Jaseong Koo(구자성),Seungjae Baeck(백승재),Byonghyo Shim(심병효) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
Recently, much research has been devoted to developing deep learning-based anomaly detection techniques. If there is a huge amount of data samples, then deep learning models can be trained to provide an end-to-end solution for anomaly detection. However, in many realistic scenarios, the number of anomalies that can be used for training deep learning models is very limited. In this case, deep learning models trained in the manner of supervised learning performs poor, especially when detecting anomalies in the test phase. In this paper, to overcome this limitation, we put forth an autoencoder-based anomaly detection technique. Through the simulations based on the dataset provided by Samsung display, we show that the proposed technique can detect anomalies well.