  We present a novel GAN-based model that utilizes the space of deep features
learned by a pre-trained classification model. Inspired by classical image
pyramid representations, we construct our model as a Semantic Generation
Pyramid -- a hierarchical framework which leverages the continuum of semantic
information encapsulated in such deep features; this ranges from low level
information contained in fine features to high level, semantic information
contained in deeper features. More specifically, given a set of features
extracted from a reference image, our model generates diverse image samples,
each with matching features at each semantic level of the classification model.
We demonstrate that our model results in a versatile and flexible framework
that can be used in various classic and novel image generation tasks. These
include: generating images with a controllable extent of semantic similarity to
a reference image, and different manipulation tasks such as
semantically-controlled inpainting and compositing; all achieved with the same
model, with no further training.
