Can this be used for anomaly detection?
sambalshikhar opened this issue · comments
Hi,
Yes, of course. The purpose of autoencoders is to compress the data, then rebuild it. Along this process, the main properties of the data are kept (in order to reduce the loss on average), however unexpected variations, that are more than noise, and happen according to random patterns (if this is an acceptable definition of an anomaly), are removed in the reconstruction phase.
Really, think of it as a process summarizing the main idea of your data.
So, if your network if good enought to rebuild the main shapes of your data, while removing these unexpected variations, then you could expect it to remove anomalies (by focusing on where the reconstruction error is 'big', i.e > a certain threshold carefully chosen).