Network Bending: Expressive Manipulation of Deep Generative Models
This provides artists and researchers with tools for expressive manipulation of generative models, though it is incremental in applying existing transformations to new contexts.
The authors introduced network bending, a framework for manipulating deep generative models by inserting deterministic transformations during inference and clustering features based on spatial activation maps, enabling direct control over semantically meaningful aspects of image generation.
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.