Interpretable Partitioned Embedding for Customized Multi-item Fashion Outfit Composition

Abstract

Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the unexplainable characteristic makes such deep learning based approach cannot meet the the designer, businesses and consumers' urge to comprehend the importance of different attributes in an outfit composition. To realize interpretable and customized fashion outfit compositions, we propose a partitioned embedding network to learn interpretable representations from clothing items. The overall network architecture consists of three components: an auto-encoder module, a supervised attributes module and a multi-independent module. The auto-encoder module serves to encode all useful information into the embedding. In the supervised attributes module, multiple attributes labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the multi-independent module, adversarial operation are adopted to fulfill the mutually independent constraint. With the interpretable and partitioned embedding, we then construct an outfit composition graph and an attribute matching map. Given specified attributes description, our model can recommend a ranked list of outfit composition with interpretable matching scores. Extensive experiments demonstrate that 1) the partitioned embedding have unmingled parts which corresponding to different attributes and 2) outfits recommended by our model are more desirable in comparison with the existing methods.

Neural Style Transfer: A Review

“…Representation disentangling. The goal of representation disentangling is to learn dimension-wise interpretable representations, where some changes in one or more specific dimensions correspond to changes precisely in a single factor of variation while being invariant to other factors [114], [115], [116], [117]. Such representations are useful to a variety of machine learning tasks, e.g., visual concepts [118] and transfer learning [119].…”

Regularizing Reasons for Outfit Evaluation with Gradient Penalty

“…They mixed all attributes and found the informative attribute crosses statistically, whereas we find the dominant factor by mimicking the analyzing process of fashion experts. Feng et al [9] proposed a partitioned embedding network, where color, shape and texture are defined as the main factors, and the score of each factor is used as the explanation. Lin et al [19] presented a recommendation system and used the generated comments as explanations.…”

Context-Aware Visual Compatibility Prediction

“…In its ideal form, it involves understanding the visual styles of garments, being cognizant of social and cultural attitudes, and making sure that when worn together the outfit is aesthetically pleasing. The task is fundamental to a variety of industry applications such as personalized fashion design [19], outfit composition [7], wardrobe creation [16], item recommendation [31] and fashion trend forecasting [1]. Fashion compatibility, however, is a complex task that depends on subjective notions of style, context, and trend -all properties that may vary from one individual to another and evolve over time.…”