Network Bending: Expressive Manipulation of Deep Generative Models

TL;DR

Network Bending introduces a framework for manipulating deep generative models through deterministic transformations and feature clustering, enabling expressive and semantically meaningful image generation during inference.

Contribution

It presents a novel framework for manipulating generative models with deterministic layers and an unsupervised feature clustering algorithm for semantic control.

Findings

Enables direct manipulation of semantic features in generated images

Provides a method for unsupervised feature clustering based on spatial activation

Demonstrates expressive manipulation on state-of-the-art models

Abstract

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.