  Deep neural networks have experimentally demonstrated superior performance
over other machine learning approaches in decision-making predictions. However,
one major concern is the closed set nature of the classification decision on
the trained classes, which can have serious consequences in safety critical
systems. When the deep neural network is in a streaming environment, fast
interpretation of this classification is required to determine if the
classification result is trusted. Un-trusted classifications can occur when the
input data to the deep neural network changes over time. One type of change
that can occur is concept evolution, where a new class is introduced that the
deep neural network was not trained on. In the majority of deep neural network
architectures, the only option is to assign this instance to one of the classes
it was trained on, which would be incorrect. The aim of this research is to
detect the arrival of a new class in the stream. Existing work on interpreting
deep neural networks often focuses on neuron activations to provide visual
interpretation and feature extraction. Our novel approach, coined DeepStreamCE,
uses streaming approaches for real-time concept evolution detection in deep
neural networks. DeepStreamCE applies neuron activation reduction using an
autoencoder and MCOD stream-based clustering in the offline phase. Both outputs
are used in the online phase to analyse the neuron activations in the evolving
stream in order to detect concept evolution occurrence in real time. We
evaluate DeepStreamCE by training VGG16 convolutional neural networks on
combinations of data from the CIFAR-10 dataset, holding out some classes to be
used as concept evolution. For comparison, we apply the data and VGG16 networks
to an open-set deep network solution - OpenMax. DeepStreamCE outperforms
OpenMax when identifying concept evolution for our datasets.
