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Rewriting a Deep Generative Model

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12346)

Abstract

A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.

Notes

Acknowledgements

We thank Jonas Wulff, Hendrik Strobelt, Aaron Hertzman, Taesung Park, William Peebles, Gerald Sussman, and William T. Freeman for their vision, encouragement, and many valuable discussions. We are grateful for the support of DARPA XAI FA8750-18-C-0004, DARPA SAIL-ON HR0011-20-C-0022, NSF 1524817 on Advancing Visual Recognition with Feature Visualizations, NSF BIGDATA 1447476, and a hardware donation from NVIDIA.

Supplementary material

Supplementary material 1 (mp4 96023 KB)

500725_1_En_21_MOESM2_ESM.pdf (4.1 mb)
Supplementary material 2 (pdf 4234 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.MIT CSAILCambridgeUSA
  2. 2.Adobe ResearchSan JoseUSA

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