Improving Face Recognition by Clustering Unlabeled Faces in the Wild
ECCV 2020 | We propose a novel identity separation method based on extreme value theory. It is formulated as an out-of-distribution detection algorithm, and it greatly reduces the problems caused by overlapping-identity label noise. Considering cluster assignments as pseudo-labels, we must also overcome the labeling noise from clustering errors. We propose a modulation of the cosine loss, where the modulation weights correspond to an estimate of clustering uncertainty.
Collaborators: Aruni Roychowdhury, Kihyuk Sohn, Erik Learned-Miller, Manmohan Chandraker