Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders
This addresses the critical issue of anomaly detection for machine learning applications, but it appears incremental as it builds on existing adversarial autoencoder methods.
The paper tackles the problem of out-of-distribution (OOD) sample detection in machine learning by proposing an Adversarial Mirrored Autoencoder (AMA) with a mirrored Wasserstein loss and latent space regularization, along with a new anomaly score measure, resulting in improved performance on several OOD detection benchmarks.
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.