We propose RaPP, a new methodology for novelty detection by utilizing hidden space activation values obtained from a deep autoencoder.
Precisely, RaPP compares input and its autoencoder reconstruction not only in the input space but also in the hidden spaces.
We show that if we feed a reconstructed input to the same autoencoder again, its activated values in a hidden space are equivalent to the corresponding reconstruction in that hidden space given the original input.
In order to aggregate the hidden space activation values, we propose two metrics, which enhance the novelty detection performance.
Through extensive experiments using diverse datasets, we validate that RaPP improves novelty detection performances of autoencoder-based approaches.
Besides, we show that RaPP outperforms recent novelty detection methods evaluated on popular benchmarks.