Unsupervised Open Set Recognition using Adversarial Autoencoders

We propose an unsupervised open set recognition approach that uses an Adversarial Autoencoder (AAE) for clustering and combines it with ii-loss to learn a representation that facilitates open set recognition. We evaluate our approach on two malware datasets. To show the applicability of the proposed method outside the malware domain we also evaluate it with an image dataset. Our proposed approach gives a statistically significant improvement over other evaluated approaches.