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Found 4 papers, 1 papers with code
PrivacyGAN: robust generative image privacy
Classical techniques for protecting facial image privacy typically fall into two categories: data-poisoning methods, exemplified by Fawkes, which introduce subtle perturbations to images, or anonymization methods that generate images resembling the original only in several characteristics, such as gender, ethnicity, or facial expression. In this study, we introduce a novel approach, PrivacyGAN, that uses the power of image generation techniques, such as VQGAN and StyleGAN, to safeguard privacy while maintaining image usability, particularly for social media applications.
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation
Latent diffusion models excel at producing high-quality images from text.
Fairness in generative modeling
We design general-purpose algorithms for addressing fairness issues and mode collapse in generative modeling.
EvolGAN: Evolutionary Generative Adversarial Networks
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both.
