  Several techniques to map various types of components, such as words,
attributes, and images, into the embedded space have been studied. Most of them
estimate the embedded representation of target entity as a point in the
projective space. Some models, such as Word2Gauss, assume a probability
distribution behind the embedded representation, which enables the spread or
variance of the meaning of embedded target components to be captured and
considered in more detail. We examine the method of estimating embedded
representations as probability distributions for the interpretation of
fashion-specific abstract and difficult-to-understand terms. Terms, such as
"casual," "adult-casual,'' "beauty-casual," and "formal," are extremely
subjective and abstract and are difficult for both experts and non-experts to
understand, which discourages users from trying new fashion. We propose an
end-to-end model called dual Gaussian visual-semantic embedding, which maps
images and attributes in the same projective space and enables the
interpretation of the meaning of these terms by its broad applications. We
demonstrate the effectiveness of the proposed method through multifaceted
experiments involving image and attribute mapping, image retrieval and
re-ordering techniques, and a detailed theoretical/analytical discussion of the
distance measure included in the loss function.
