  Neural networks embedded in safety-sensitive applications such as
self-driving cars and wearable health monitors rely on two important
techniques: input attribution for hindsight analysis and network compression to
reduce its size for edge-computing. In this paper, we show that these seemingly
unrelated techniques conflict with each other as network compression deforms
the produced attributions, which could lead to dire consequences for
mission-critical applications. This phenomenon arises due to the fact that
conventional network compression methods only preserve the predictions of the
network while ignoring the quality of the attributions. To combat the
attribution inconsistency problem, we present a framework that can preserve the
attributions while compressing a network. By employing the Weighted Collapsed
Attribution Matching regularizer, we match the attribution maps of the network
being compressed to its pre-compression former self. We demonstrate the
effectiveness of our algorithm both quantitatively and qualitatively on diverse
compression methods.
