  Disentangling content and style information of an image has played an
important role in recent success in image translation. In this setting, how to
inject given style into an input image containing its own content is an
important issue, but existing methods followed relatively simple approaches,
leaving room for improvement especially when incorporating significant style
changes. In response, we propose an advanced normalization technique based on
adaptive convolution (AdaCoN), in order to properly impose style information
into the content of an input image. In detail, after locally standardizing the
content representation in a channel-wise manner, AdaCoN performs adaptive
convolution where the convolution filter weights are dynamically estimated
using the encoded style representation. The flexibility of AdaCoN can handle
complicated image translation tasks involving significant style changes. Our
qualitative and quantitative experiments demonstrate the superiority of our
proposed method against various existing approaches that inject the style into
the content.
