  A main issue preventing the use of Convolutional Neural Networks (CNN) in end
user applications is the low level of transparency in the decision process.
Previous work on CNN interpretability has mostly focused either on localizing
the regions of the image that contribute to the result or on building an
external model that generates plausible explanations. However, the former does
not provide any semantic information and the latter does not guarantee the
faithfulness of the explanation. We propose an intermediate representation
composed of multiple Semantically Interpretable Activation Maps (SIAM)
indicating the presence of predefined attributes at different locations of the
image. These attribute maps are then linearly combined to produce the final
output. This gives the user insight into what the model has seen, where, and a
final output directly linked to this information in a comprehensive and
interpretable way. We test the method on the task of landscape scenicness
(aesthetic value) estimation, using an intermediate representation of 33
attributes from the SUN Attributes database. The results confirm that SIAM
makes it possible to understand what attributes in the image are contributing
to the final score and where they are located. Since it is based on learning
from multiple tasks and datasets, SIAM improve the explanability of the
prediction without additional annotation efforts or computational overhead at
inference time, while keeping good performances on both the final and
intermediate tasks.
