  Weakly-supervised anomaly detection aims at learning an anomaly detector from
a limited amount of labeled data and abundant unlabeled data. Recent works
build deep neural networks for anomaly detection by discriminatively mapping
the normal samples and abnormal samples to different regions in the feature
space or fitting different distributions. However, due to the limited number of
annotated anomaly samples, directly training networks with the discriminative
loss may not be sufficient. To overcome this issue, this paper proposes a novel
strategy to transform the input data into a more meaningful representation that
could be used for anomaly detection. Specifically, we leverage an autoencoder
to encode the input data and utilize three factors, hidden representation,
reconstruction residual vector, and reconstruction error, as the new
representation for the input data. This representation amounts to encode a test
sample with its projection on the training data manifold, its direction to its
projection and its distance to its projection. In addition to this encoding, we
also propose a novel network architecture to seamlessly incorporate those three
factors. From our extensive experiments, the benefits of the proposed strategy
are clearly demonstrated by its superior performance over the competitive
methods.
