  There is an increasingly apparent need for validating the classifications
made by deep learning systems in safety-critical applications like autonomous
vehicle systems. A number of recent papers have proposed methods for detecting
anomalous image data that appear different from known inlier data samples,
including reconstruction-based autoencoders. Autoencoders optimize the
compression of input data to a latent space of a dimensionality smaller than
the original input and attempt to accurately reconstruct the input using that
compressed representation. Since the latent vector is optimized to capture the
salient features from the inlier class only, it is commonly assumed that images
of objects from outside of the training class cannot effectively be compressed
and reconstructed. Some thus consider reconstruction error as a kind of novelty
measure. Here we suggest that reconstruction-based approaches fail to capture
particular anomalies that lie far from known inlier samples in latent space but
near the latent dimension manifold defined by the parameters of the model. We
propose incorporating the Mahalanobis distance in latent space to better
capture these out-of-distribution samples and our results show that this method
often improves performance over the baseline approach.
