Fairness and Bias in Generative Models

Fairness and Bias in Generative Models

Recent advances in generative adversarial networks have shown that it is possible to generate high-resolution and hyperrealistic images. However, the images produced by GANs are only as fair and representative as the datasets on which they are trained. We will develop approaches to investigate and debias GAN models.

Project Poster: 

Project Members: 

Kıymet Akdemir
Aleyna Kara

Project Advisor: 

Suzan Üsküdarlı

Project Status: 

Project Year: 

  • Spring

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Department of Computer Engineering, Boğaziçi University,
34342 Bebek, Istanbul, Turkey

  • Phone: +90 212 359 45 23/24
  • Fax: +90 212 2872461

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