Generative Adversarial Networks Unlearning

Understand

As machine learning continues to develop, and data misuse scandals become more prevalent, individuals are becoming increasingly concerned about their personal information and are advocating for the right to remove their data.

• Machine unlearning has emerged as a solution to erase training data from trained machine learning models.
• Despite its success in classifiers, research on Generative Adversarial Networks (GANs) is limited due to their unique architecture, including a generator and a discriminator.
• One challenge pertains to generator unlearning, as the process could potentially disrupt the continuity and completeness of the latent space.