  Convolutional Neural Networks have achieved impressive results in various
tasks, but interpreting the internal mechanism is a challenging problem. To
tackle this problem, we exploit a multi-channel attention mechanism in feature
space. Our network architecture allows us to obtain an attention mask for each
feature while existing CNN visualization methods provide only a common
attention mask for all features. We apply the proposed multi-channel attention
mechanism to multi-attribute recognition task. We can obtain different
attention mask for each feature and for each attribute. Those analyses give us
deeper insight into the feature space of CNNs. Furthermore, our proposed
attention mechanism naturally derives a method for improving the robustness of
CNNs. From the observation of feature space based on the proposed attention
mask, we demonstrate that we can obtain robust CNNs by intentionally
emphasizing features that are important for attributes. The experimental
results for the benchmark dataset show that the proposed method gives high
human interpretability while accurately grasping the attributes of the data,
and improves network robustness.
