  When creating an outfit, style is a criterion in selecting each fashion item.
This means that style can be regarded as a feature of the overall outfit.
However, in various previous studies on outfit generation, there have been few
methods focusing on global information obtained from an outfit. To address this
deficiency, we have incorporated an unsupervised style extraction module into a
model to learn outfits. Using the style information of an outfit as a whole,
the proposed model succeeded in generating outfits more flexibly without
requiring additional information. Moreover, the style information extracted by
the proposed model is easy to interpret. The proposed model was evaluated on
two human-generated outfit datasets. In a fashion item prediction task (missing
prediction task), the proposed model outperformed a baseline method. In a style
extraction task, the proposed model extracted some easily distinguishable
styles. In an outfit generation task, the proposed model generated an outfit
while controlling its styles. This capability allows us to generate fashionable
outfits according to various preferences.
