Attribution Preservation in Network Compression for Reliable Network Interpretation
This addresses a critical problem for safety-sensitive applications like self-driving cars and health monitors by ensuring reliable network interpretation post-compression, representing a novel solution to an identified bottleneck.
The paper tackles the conflict between network compression and input attribution preservation, showing that compression deforms attributions, which is critical for safety-sensitive applications. The authors present a framework with a Weighted Collapsed Attribution Matching regularizer that effectively preserves attributions during compression, as demonstrated quantitatively and qualitatively across diverse methods.
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.