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Reprogramming GANs via Input Noise Design

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12458))

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Abstract

The goal of neural reprogramming is to alter the functionality of a fixed neural network just by preprocessing the input. In this work, we show that Generative Adversarial Networks (GANs) can be reprogrammed by shaping the input noise distribution. One application of our algorithm is to convert an unconditional GAN to a conditional GAN. We also empirically study the applicability, feasibility, and limitation of GAN reprogramming.

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA2386-19-1-4050.

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Correspondence to Changho Suh .

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Lee, K., Suh, C., Ramchandran, K. (2021). Reprogramming GANs via Input Noise Design. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12458. Springer, Cham. https://doi.org/10.1007/978-3-030-67661-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-67661-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67660-5

  • Online ISBN: 978-3-030-67661-2

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