ARES: Locally Adaptive Reconstruction-based Anomaly Scoring. (arXiv:2206.07604v1 [cs.LG])

How can we detect anomalies: that is, samples that significantly differ from
a given set of high-dimensional data, such as images or sensor data? This is a
practical problem with numerous applications and is also relevant to the goal
of making learning algorithms more robust to unexpected inputs. Autoencoders
are a popular approach, partly due to their simplicity and their ability to
perform dimension reduction. However, the anomaly scoring function is not
adaptive to the natural variation in reconstruction error …

anomaly arxiv lg scoring