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Related Experiment Video

Updated: Oct 6, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Network Bending: Expressive Manipulation of Generative Models in Multiple Domains.

Terence Broad1,2, Frederic Fol Leymarie1, Mick Grierson2

  • 1Department of Computing, Goldsmiths, University of London, London SE14 6NW, UK.

Entropy (Basel, Switzerland)
|January 21, 2022
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Summary

This study introduces network bending for deep generative models, enabling direct manipulation of semantic features. This framework allows for meaningful control over generated content in image and audio domains.

Keywords:
active divergencedeep generative modelsexpressive manipulation

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • Deep generative models create complex data like images and audio.
  • Interacting with and controlling these models during generation is challenging.

Purpose of the Study:

  • Introduce the network bending framework for manipulating deep generative models.
  • Develop methods for analyzing and clustering features within these models.

Main Methods:

  • Implemented deterministic transformations as layers in the computational graph.
  • Developed an unsupervised algorithm for clustering features based on spatial activation maps.

Main Results:

  • Demonstrated effective manipulation of semantically meaningful features in image and audio generation.
  • Showcased the framework's ability to produce a broad range of expressive outcomes.
  • Enabled unsupervised grouping of features based on spatial similarity.

Conclusions:

  • Network bending offers a novel approach to control and interact with deep generative models.
  • The framework facilitates direct manipulation of semantic aspects of the generative process.
  • Results show broad applicability across different data domains.