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Generative Adversarial Network Technologies and Applications in Computer Vision.

Lianchao Jin1, Fuxiao Tan1, Shengming Jiang1

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Computational Intelligence and Neuroscience
|August 18, 2020
PubMed
Summary
This summary is machine-generated.

Generative Adversarial Networks (GANs) offer advanced feature learning and image generation in deep learning, improving computer vision tasks despite challenges like model collapse. This review explores GANs

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Generative Adversarial Networks (GANs) represent a significant advancement in deep learning for computer vision.
  • GANs utilize a game-theoretic training approach, outperforming traditional machine learning in feature learning and image generation.
  • While powerful, GANs face challenges such as model collapse and training instability.

Purpose of the Study:

  • To provide a comprehensive review of the theoretical underpinnings of GANs.
  • To survey and compare recent GAN models with traditional architectures.
  • To explore the diverse applications and future directions of GANs in computer vision.

Main Methods:

  • Theoretical review of Generative Adversarial Networks.
  • Comparative analysis of traditional and contemporary GAN models.
  • Survey of GAN applications across various AI domains.

Main Results:

  • GANs demonstrate superior performance in feature learning and image generation compared to traditional methods.
  • Key applications include data enhancement, domain transfer, high-quality sample generation, and image restoration.
  • Recent progress highlights GANs' role in AI-driven security attacks and defenses.

Conclusions:

  • GANs are a transformative technology in computer vision, with ongoing research addressing limitations.
  • Future developments promise expanded applications in AI and computer vision.
  • Continued exploration of GANs is crucial for advancing AI capabilities.