  Network interpretation as an effort to reveal the features learned by a
network remains largely visualization-based. In this paper, our goal is to
tackle semantic network interpretation at both filter and decision level. For
filter-level interpretation, we represent the concepts a filter encodes with a
probability distribution of visual attributes. The decision-level
interpretation is achieved by textual summarization that generates an
explanatory sentence containing clues behind a network's decision. A Bayesian
inference algorithm is proposed to automatically associate filters and network
decisions with visual attributes. Human study confirms that the semantic
interpretation is a beneficial alternative or complement to visualization
methods. We demonstrate the crucial role that semantic network interpretation
can play in understanding a network's failure patterns. More importantly,
semantic network interpretation enables a better understanding of the
correlation between a model's performance and its distribution metrics like
filter selectivity and concept sparseness.
