train a generative adversarial network (GAN) to generate multi-view images from a single-view input. For that, the GAN architecture proposed in [@Tian2018CR-GAN:Generation] is employed. Following the experiment setup in [@Tian2018CR-GAN:Generation], only $9$ poses between $\frac{\pi}{6}$ and $\frac{5\pi}{6}$ are considered. Furthermore, the first $200$ subjects are for training and the rest are for testing. Thus, the total size of the training set is $72,000$, $360$ per subject. All the implementation details are same as [@Tian2018CR-GAN:Generation], unless otherwise is stated[^7].

![Multi-view face generation results for a sample subject in Multi-PIE [@Gross2010Multi-PIE] testing set using CR-GAN [@Tian2018CR-GAN:Generation]. The network is trained on reduced training set ($9$ images per subject) using random selection (first row), K-medoids (second row), DS3 [@Elhamifar2016DissimilaritySelection] (third row), and IPM (fourth row). The fifth row shows the results generated by the network trained on all the data ($360$ images per subject). IPM-reduced dataset generates closest results to the complete dataset.[]{data-label="fig:PIE-GAN"}](Figures/GAN_FNA.png){width="1\columnwidth"}

We select only $9$ images from each subject ($1800$ total), and train the network with the reduced dataset for $300$ epochs using the batch size of $36$. Figure \[fig:PIE-GAN\] shows the generated images of a subject in the testing set, using the trained network on the reduced dataset, as well 