  Attention Branch Networks (ABNs) have been shown to simultaneously provide
visual explanation and improve the performance of deep convolutional neural
networks (CNNs). In this work, we introduce Multi-Scale Attention Branch
Networks (MSABN), which enhance the resolution of the generated attention maps,
and improve the performance. We evaluate MSABN on benchmark image recognition
and fine-grained recognition datasets where we observe MSABN outperforms ABN
and baseline models. We also introduce a new data augmentation strategy
utilizing the attention maps to incorporate human knowledge in the form of
bounding box annotations of the objects of interest. We show that even with a
limited number of edited samples, a significant performance gain can be
achieved with this strategy.
