Deep Learning models are known for their capacity to memorize training data, a characteristic that can lead to significant privacy concerns.

In this talk, we will delve into the implications of this memorization and explore strategies to mitigate its effects. We will discuss practical methods to reduce unintended memorization and their results, providing insights into how these techniques can be deployed in real-world scenarios.

Tom’s work: https://scholar.google.com/citations?user=xrewx-sAAAAJ&hl=en

Audience: technique