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Inverting the Generator of a Generative Adversarial Network

IEEE transaction on neural networks and learning systems, 2019-07, Vol.30 (7), p.1967-1974

ISSN: 2162-237X ;EISSN: 2162-2388 ;DOI: 10.1109/TNNLS.2018.2875194 ;PMID: 30403640 ;CODEN: ITNNAL

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  • Title:
    Inverting the Generator of a Generative Adversarial Network
  • Author: Creswell, Antonia ; Bharath, Anil Anthony
  • Subjects: Backpropagation ; Data models ; feature extraction ; Gallium nitride ; Generative adversarial networks ; Generators ; image generation ; Image reconstruction ; Learning systems ; multilayer neural network ; pattern recognition ; Training ; unsupervised learning
  • Is Part Of: IEEE transaction on neural networks and learning systems, 2019-07, Vol.30 (7), p.1967-1974
  • Description: Generative adversarial networks (GANs) learn a deep generative model that is able to synthesize novel, high-dimensional data samples. New data samples are synthesized by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties that may be useful for downstream tasks such as classification or retrieval. Unfortunately, GANs do not offer an "inverse model," a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample. In this paper, we introduce a technique, inversion , to project data samples, specifically images, to the latent space using a pretrained GAN. Using our proposed inversion technique, we are able to identify which attributes of a data set a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss. We demonstrate how our proposed inversion technique may be used to quantitatively compare the performance of various GAN models trained on three image data sets. We provide codes for all of our experiments in the website ( https://github.com/ToniCreswell/InvertingGAN ).
  • Publisher: IEEE
  • Language: English
  • Identifier: ISSN: 2162-237X
    EISSN: 2162-2388
    DOI: 10.1109/TNNLS.2018.2875194
    PMID: 30403640
    CODEN: ITNNAL
  • Source: IEEE Open Access Journals
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