  In this paper, we build an outfit evaluation system which provides feedbacks
consisting of a judgment with a convincing explanation. The system is trained
in a supervised manner which faithfully follows the domain knowledge in
fashion. We create the EVALUATION3 dataset which is annotated with judgment,
the decisive reason for the judgment, and all corresponding attributes (e.g.
print, silhouette, and material \etc.). In the training process, features of
all attributes in an outfit are first extracted and then concatenated as the
input for the intra-factor compatibility net. Then, the inter-factor
compatibility net is used to compute the loss for judgment. We penalize the
gradient of judgment loss of so that our Grad-CAM-like reason is regularized to
be consistent with the labeled reason. In inference, according to the obtained
information of judgment, reason, and attributes, a user-friendly explanation
sentence is generated by the pre-defined templates. The experimental results
show that the obtained network combines the advantages of high precision and
good interpretation.
