  We propose a neural network for unsupervised anomaly detection with a novel
robust subspace recovery layer (RSR layer). This layer seeks to extract the
underlying subspace from a latent representation of the given data and removes
outliers that lie away from this subspace. It is used within an autoencoder.
The encoder maps the data into a latent space, from which the RSR layer
extracts the subspace. The decoder then smoothly maps back the underlying
subspace to a "manifold" close to the original inliers. Inliers and outliers
are distinguished according to the distances between the original and mapped
positions (small for inliers and large for outliers). Extensive numerical
experiments with both image and document datasets demonstrate state-of-the-art
precision and recall.
