  We introduce a new framework for manipulating and interacting with deep
generative models that we call network bending. We present a comprehensive set
of deterministic transformations that can be inserted as distinct layers into
the computational graph of a trained generative neural network and applied
during inference. In addition, we present a novel algorithm for analysing the
deep generative model and clustering features based on their spatial activation
maps. This allows features to be grouped together based on spatial similarity
in an unsupervised fashion. This results in the meaningful manipulation of sets
of features that correspond to the generation of a broad array of semantically
significant features of the generated images. We outline this framework,
demonstrating our results on state-of-the-art deep generative models trained on
several image datasets. We show how it allows for the direct manipulation of
semantically meaningful aspects of the generative process as well as allowing
for a broad range of expressive outcomes.
