  Deep convolutional neural networks (CNNs) have achieved breakthrough
performance in many pattern recognition tasks such as image classification.
However, the development of high-quality deep models typically relies on a
substantial amount of trial-and-error, as there is still no clear understanding
of when and why a deep model works. In this paper, we present a visual
analytics approach for better understanding, diagnosing, and refining deep
CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this
formulation, a hybrid visualization is developed to disclose the multiple
facets of each neuron and the interactions between them. In particular, we
introduce a hierarchical rectangle packing algorithm and a matrix reordering
algorithm to show the derived features of a neuron cluster. We also propose a
biclustering-based edge bundling method to reduce visual clutter caused by a
large number of connections between neurons. We evaluated our method on a set
of CNNs and the results are generally favorable.
