  In one-class novelty detection, a model learns solely on the in-class data to
single out out-class instances. Autoencoder (AE) variants aim to compactly
model the in-class data to reconstruct it exclusively, thus differentiating the
in-class from out-class by the reconstruction error. However, compact modeling
in an improper way might collapse the latent representations of the in-class
data and thus their reconstruction, which would lead to performance
deterioration. Moreover, to properly measure the reconstruction error of
high-dimensional data, a metric is required that captures high-level semantics
of the data. To this end, we propose Discriminative Compact AE (DCAE) that
learns both compact and collapse-free latent representations of the in-class
data, thereby reconstructing them both finely and exclusively. In DCAE, (a) we
force a compact latent space to bijectively represent the in-class data by
reconstructing them through internal discriminative layers of generative
adversarial nets. (b) Based on the deep encoder's vulnerability to open set
risk, out-class instances are encoded into the same compact latent space and
reconstructed poorly without sacrificing the quality of in-class data
reconstruction. (c) In inference, the reconstruction error is measured by a
novel metric that computes the dissimilarity between a query and its
reconstruction based on the class semantics captured by the internal
discriminator. Extensive experiments on public image datasets validate the
effectiveness of our proposed model on both novelty and adversarial example
detection, delivering state-of-the-art performance.
