论文标题
定期评估服装评估的原因
Regularizing Reasons for Outfit Evaluation with Gradient Penalty
论文作者
论文摘要
在本文中，我们构建了一个服装评估系统，该系统提供了由判断和令人信服的解释组成的反馈。该系统以监督的方式进行了训练，该系统忠实地遵循了时尚领域的知识。我们创建了带有判断的判断力，判断的决定性原因以及所有相应属性（例如打印，剪影和材料\等）的判断，决定性的原因和所有相应属性的数据集。在训练过程中，首先提取服装中所有属性的特征，然后将其作为因子内兼容性网的输入。然后，使用因素间兼容性网计算判断的损失。我们对判决损失的梯度进行惩罚，以使我们的类似于毕业的理性的理由正规化，以与标有理由一致。在推论中，根据获得的判断，原因和属性的信息，预定义的模板生成了用户友好的解释句子。实验结果表明，获得的网络结合了高精度和良好解释的优势。
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.
