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Deep learning techniques are increasingly being adopted for classificationtasks over the past decade, yet explaining how deep learning architectures canachieve state-of-the-art performance is still an elusive goal. While all thetraining information is embedded deeply in a trained model, we still do notunderstand much about its performance by only analyzing the model. This paperexamines the neuron activation patterns of deep learning-based classificationmodels and explores whether the models' performances can be explained throughneurons' activation behavior. We propose two approaches: one that modelsneurons' activation behavior as a graph and examines whether the neurons formmeaningful communities, and the other examines the predictability of neurons'behavior using entropy. Our comprehensive experimental study reveals that boththe community quality and entropy can provide new insights into the deeplearning models' performances, thus paves a novel way of explaining deeplearning models directly from the neurons' activation pattern.

On the Evolution of Neuron Communities in a Deep Learning Architecture