TracInAD: Measuring Influence for Anomaly Detection. (arXiv:2205.01362v1 [cs.LG])

Web: http://arxiv.org/abs/2205.01362

As with many other tasks, neural networks prove very effective for anomaly
detection purposes. However, very few deep-learning models are suited for
detecting anomalies on tabular datasets. This paper proposes a novel
methodology to flag anomalies based on TracIn, an influence measure initially
introduced for explicability purposes. The proposed methods can serve to
augment any unsupervised deep anomaly detection method. We test our approach
using Variational Autoencoders and show that the average influence of a
subsample of training points on …

anomaly detection arxiv detection influence