  We propose a novel explanation method that explains the decisions of a deep
neural network by investigating how the intermediate representations at each
layer of the deep network were refined during the training process. This way we
can a) find the most influential training examples during training and b)
analyze which classes attributed most to the final representation. Our method
is general: it can be wrapped around any iterative optimization procedure and
covers a variety of neural network architectures, including feed-forward
networks and convolutional neural networks. We first propose a method for
stochastic training with single training instances, but continue to also derive
a variant for the common mini-batch training. In experimental evaluations, we
show that our method identifies highly representative training instances that
can be used as an explanation. Additionally, we propose a visualization that
provides explanations in the form of aggregated statistics over the whole
training process.
