Complex Background Reconstruction for Novelty Detection

Novelty detection aims to detect samples from classes different from the training samples (i.e., the normal class). Existing approaches predominantly make the target reconstruction better and choose the appropriate reconstruction error measurement method but ignore the influence of background information on this process. This paper proposes a novel reconstruction network and mutual information Siamese network. The reconstructed network aims to make the distribution of reconstructed samples consistent with that of original samples, intending to reduce background interference in the reconstruction process. After this, we measure the distance between the original and generated images based on a mutual information Siamese network, which extracts more discriminative features to calculate the similarity between the original images and their reconstructed ones. This part of the network uses global context information to improve the detection accuracy. We conduct extreme experiments to evaluate the proposed solution on two challenging public datasets. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods.