  Query reformulation is the process by which a input search query is refined
by the user to match documents outside the original top-n results. On average,
roughly 50% of text search queries involve some form of reformulation, and term
suggestion tools are used 35% of the time when offered to users. As prevalent
as text search queries are, however, such a feature has yet to be explored at
scale for visual search. This is because reformulation for images presents a
novel challenge to seamlessly transform visual features to match user intent
within the context of a typical user session. In this paper, we present methods
of semantically transforming visual queries, such as utilizing operations in
the latent space of a generative adversarial model for the scenarios of fashion
and product search.
