Improving the Fairness of Deep Generative Models without Retraining

Understand

Generative Adversarial Networks (GANs) advance face synthesis through learning the underlying distribution of observed data.

• Despite the high-quality generated faces, some minority groups can be rarely generated from the trained models due to a biased image generation process.
• To study the issue, we first conduct an empirical study on a pre-trained face synthesis model.
• We observe that after training the GAN model not only carries the biases in the training data but also amplifies them to some degree in the image generation process.