  A common assumption of novelty detection is that the distribution of both
"normal" and "novel" data are static. This, however, is often not the case -
for example scenarios where data evolves over time or scenarios in which the
definition of normal and novel depends on contextual information, both leading
to changes in these distributions. This can lead to significant difficulties
when attempting to train a model on datasets where the distribution of normal
data in one scenario is similar to that of novel data in another scenario. In
this paper we propose a context-aware approach to novelty detection for deep
autoencoders to address these difficulties. We create a semi-supervised network
architecture that utilises auxiliary labels to reveal contextual information
and allow the model to adapt to a variety of contexts in which the definitions
of normal and novel change. We evaluate our approach on both image data and
real world audio data displaying these characteristics and show that the
performance of individually trained models can be achieved in a single model.
