  Detecting out of distribution (OOD) samples is of paramount importance in all
Machine Learning applications. Deep generative modeling has emerged as a
dominant paradigm to model complex data distributions without labels. However,
prior work has shown that generative models tend to assign higher likelihoods
to OOD samples compared to the data distribution on which they were trained.
First, we propose Adversarial Mirrored Autoencoder (AMA), a variant of
Adversarial Autoencoder, which uses a mirrored Wasserstein loss in the
discriminator to enforce better semantic-level reconstruction. We also propose
a latent space regularization to learn a compact manifold for in-distribution
samples. The use of AMA produces better feature representations that improve
anomaly detection performance. Second, we put forward an alternative measure of
anomaly score to replace the reconstruction-based metric which has been
traditionally used in generative model-based anomaly detection methods. Our
method outperforms the current state-of-the-art methods for anomaly detection
on several OOD detection benchmarks.
