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Generative Adversarial Networks: An Overview

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arxiv 1710.07035 v1 pith:ZEMMCDZO submitted 2017-10-19 cs.CV

Antonia Creswell , Tom White , Vincent Dumoulin , Kai Arulkumaran , Biswa Sengupta , Anil A Bharath This is my paper · ORCID
classification cs.CV
keywords gansimagenetworksadversarialgenerativeoverviewrepresentationstraining
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Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Case Studies of Generative Machine Learning Models for Dynamical Systems

    eess.SY 2025-08 conditional novelty 5.0 of 10

    Physics-informed VAEs with Hamiltonian-based losses generate trajectories that match training distributions and satisfy optimal-control equations from as few as 200 to 500 samples.

  2. Mal-D2GAN: Double-Detector based GAN for Malware Generation

    cs.CR 2025-05 reject novelty 4.0 of 10

    Mal-D2GAN, a GAN with two detectors and a least-squares loss, produced adversarial malware that lowered the true positive rate of eight classifiers to near zero on a 20,000-sample dataset.

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