Ünsal Öztürk

Quantifying Training Membership Information in the Hyperspherical Embedding Geometry of Face Recognition Models

The results indicate that the number of training identities has the largest effect on member/non-member separability, while backbone and loss head contribute far less, and that, on a same-domain held-out reference, the geometric membership signal decreases monotonically as more identities are added to training.