3 accepted papers
The success of multi-modal foundational models can be partly attributed to their diverse, billions scale training data. By nature, web data contains human faces and descriptions of individuals. Thus, these models pose potentially widespread privacy issues. Recently, identity membership inference att…
State of the art deep generative networks have achieved such realism that they can be suspected of memorizing training images. It is why it is not uncommon to include visualizations of training set nearest neighbors, to suggest generated images are not simply memorized. We argue this is not sufficie…
In this article we revisit the definition of Precision-Recall (PR) curves for generative models proposed by (Sajjadi et al., 2018). Rather than providing a scalar for generative quality, PR curves distinguish mode-collapse (poor recall) and bad quality (poor precision). We first generalize their for…