Quantifying and Mitigating Privacy Risks of Contrastive Learning

Understand

Data is the key factor to drive the development of machine learning (ML) during the past decade.

• However, high-quality data, in particular labeled data, is often hard and expensive to collect.
• To leverage large-scale unlabeled data, self-supervised learning, represented by contrastive learning, is introduced.
• The objective of contrastive learning is to map different views derived from a training sample (e.g., through data augmentation) closer in their representation space, while different views derived from different samples more distant.