Neural Network Training Strategy to Enhance Anomaly Detection
  Performance: A Perspective on Reconstruction Loss Amplification
YeongHyeon Park Sungho Kang Myung Jin Kim Hyeonho Jeong Hyunkyu Park Hyeong Seok Kim Juneho Yi
Unsupervised anomaly detection (UAD) is a widely adopted approach in industry
due to rare anomaly occurrences and data imbalance. A desirable characteristic
of an UAD model is contained generalization ability which excels in the
reconstruction of seen normal patterns but struggles with unseen anomalies.
Recent studies have pursued to contain the generalization capability of their
UAD models in reconstruction from different perspectives, such as design of
neural network (NN) structure and training strategy. In contrast, we note that
containing of generalization ability in reconstruction can also be obtained
simply from steep-shaped loss landscape. Motivated by this, we propose a loss
landscape sharpening method by amplifying the reconstruction loss, dubbed Loss
AMPlification (LAMP). LAMP deforms the loss landscape into a steep shape so the
reconstruction error on unseen anomalies becomes greater. Accordingly, the
anomaly detection performance is improved without any change of the NN
architecture. Our findings suggest that LAMP can be easily applied to any
reconstruction error metrics in UAD settings where the reconstruction model is
trained with anomaly-free samples only.