as using the complete dataset. The network trained on samples selected by IPM (fourth row) is able to generate more realistic images, with fewer artifacts, compared to other selection methods (rows 1-3). Furthermore, compared to the results using all the data (row 5), it is clear that IPM-reduced dataset generates the closest results to the complete dataset. This is because, as demonstrated in Figure \[fig:PIE\_selection\], samples selected by IPM cover more angles of the subject, leading better training of the GAN. See supplementary material for further experiments and sample outputs.

  Method                    Random   K-Medoids    DS3          IPM
  ------------------------ -------- ----------- -------- ---------------
  $9$ images / subject      0.5616    0.5993     0.6022   [**0.553**]{}
  $360$ images / subject                                 

  :  Identity dissimilarities between real and generated images by network trained on reduced (using different selection methods) and complete dataset. []{data-label="tab:GAN"}

For a quantitative performance investigation, we evaluate the identity similarities between the real and generated images. For that, we feed each pair of real and generated images to a ResNet18[^8], trained on MS-Celeb-1M dataset [@Guo2016MS-Celeb-1M:World], and obtain 256-dimensional features. $\ell_2$ distances of features correspond to the face dissimilarity. Table \[tab:GAN\] shows the normalized $\ell_2$ distances between the real and generated images, averaged over all the images in 