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In this paper, we build an outfit evaluation system which provides feedbacksconsisting of a judgment with a convincing explanation. The system is trainedin a supervised manner which faithfully follows the domain knowledge infashion. 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 ofall attributes in an outfit are first extracted and then concatenated as theinput for the intra-factor compatibility net. Then, the inter-factorcompatibility net is used to compute the loss for judgment. We penalize thegradient of judgment loss of so that our Grad-CAM-like reason is regularized tobe consistent with the labeled reason. In inference, according to the obtainedinformation of judgment, reason, and attributes, a user-friendly explanationsentence is generated by the pre-defined templates. The experimental resultsshow that the obtained network combines the advantages of high precision andgood interpretation.

Regularizing Reasons for Outfit Evaluation with Gradient Penalty