the testing set. Our method outperforms other selection methods in this metric as well. Thus, from Figure \[fig:PIE-GAN\] (qualitative) and Table \[tab:GAN\] (quantitative), we can conclude that the IPM-reduced training set contains more information about the complete set, compared to other selection methods.

                                                  1          2          3          4          5          6      
  ------------------------------------------- ---------- ---------- ---------- ---------- ---------- ---------- --
  Random                                         54.6       64.7       69.2       70.5       72.9       74.0    
  K-medoids                                      61.0       67.7       69.4       70.9       71.7       72.0    
  DS3[@Elhamifar2016DissimilaritySelection]      60.8       69.1       74.0       75.2       74.8       75.3    
  IPM                                          **65.3**   **72.6**   **74.9**   **77.6**   **77.0**   **78.5**  

  :  Accuracy (%) of ResNet18 on UCF-101 dataset, trained using only the representatives selected by different methods. The accuracy using the full training set (9537 samples) is $82.23\%$.[]{data-label="tab:reps"}

### Finding Representatives for UCF-101 Dataset {#subsec:reps_ucf101}

Here, similar to Section \[subsec:active-learning\], we use a 3D ResNet18 classifier pretrained on Kinetics-400 dataset, and the selection algorithms are performed on feature space generated by the output of the last convolutional layer. To find the representatives, we use the selection methods to sequentially find the most informative representatives from each class. After selecting the representatives, the fully connected layer of the network is finetuned in the same manner as described in Section \[subsec:active-learning\]. Table \[tab:reps\] shows the performance of 