Network Bending: Expressive Manipulation of Generative Models in Multiple Domains

Broad, Terence; Leymarie, Frederic Fol and Grierson, Mick. 2022. Network Bending: Expressive Manipulation of Generative Models in Multiple Domains. Entropy, 24(1), 28. ISSN 1099-4300 [Article]

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Abstract or Description

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. 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 results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. 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.

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This work has been supported by UK’s EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI; grant EP/L015846/1).

The generated datasets of activation maps have been made publicly
available and can be found at:


deep generative models; expressive manipulation; active divergence

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17 December 2021Accepted
24 December 2021Published Online

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Date Deposited:

07 Feb 2022 10:20

Last Modified:

07 Feb 2022 10:29

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Yes, this version has been peer-reviewed.


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