# Extending Contrastive Learning to Unsupervised Redundancy Identification
## 5 Experiments
### Semantic Segmentation Task  
Recall that our selection method removed 1219 examples of the identified redundancy from our refined training set and we used the remaining subset (6400) to train DeepLab. In contrast, the random selection method simply chose 6400 examples out of our refined training set. In this way, there were approximately 641 and 87 examples of true redundancy in the randomly selected subset and our subset, respectively. As listed in Table 2, we displayed a semantic segmentation performance in terms of the IoU for each class and the overall performance in terms of the mIoU. We achieved a 0.5614 mIoU and an improvement of +0.0381 compared with random selection. First, we consider both the number of pixels and the IoU performance. We counted the number of pixels in the test set in terms of percentage. It was obvious that there was a strong correlation between the number of pixels and the IoU performance. Namely, as the number of pixels increased, the IoU also exhibited a higher value for both our method and random selection. Hence, because the pixels belonging to the road class occupied the highest percentage of total pixels, the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Selection** & & **Instance** & \multicolumn{2}{c}{**Performance Metric**} \\
**Method** & **Class** & **Count** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline \multirow{7}{*}{Our Selection} & all & 11,493 & 0.9326 \(\pm\) 0.0032 (+0.0060) & 0.7214 \(\pm\) 0.0047 (+0.0176) \\  & car & 8323 & 0.9810 \(\pm\) 0.0000 (+0.0028) & 0.8592 \(\pm\) 0.0011 (+0.0116) \\  & van & 805 & 0.9714 \(\pm\) 0.0024 (+0.0038) & 0.8336 \(\pm\) 0.0030 (+0.0154) \\  & truck & 313 & 0.9828 \(\pm\) 0.0031 (+0.0032) & 0.8582 \(\pm\) 0.0070 (+0.0136) \\  & pedestrian & 1110 & 0.8708 \(\pm\) 0.0069 (+0.0136) & 0.4970 \(\pm\) 0.0041 (+0.0158) \\  & person sitting & 73 & 0.7764 \(\pm\) 0.0228 (\(-\)0.0132) & 0.4584 \(\pm\) 0.0260 (+0.0042) \\  & cyclist & 439 & 0.9402 \(\pm\) 0.0043 (+0.0112) & 0.7034 \(\pm\) 0.0050 (+0.0246) \\  & tram & 157 & 0.9674 \(\pm\) 0.0078 (+0.0106) & 0.7628 \(\pm\) 0.0106 (+0.0226) \\  & misc & 273 & 0.9712 \(\pm\) 0.0036 (+0.0172) & 0.7712 \(\pm\) 0.0024 (+0.0332) \\ \hline \multirow{7}{*}{Random Selection} & all & 11,493 & 0.9266 \(\pm\) 0.0043 & 0.7038 \(\pm\) 0.0024 \\  & car & 8323 & 0.9782 \(\pm\) 0.0011 & 0.8476 \(\pm\) 0.0023 \\ \cline{1-1}  & van & 805 & 0.9676 \(\pm\) 0.0024 & 0.8182 \(\pm\) 0.0011 \\ \cline{1-1}  & truck & 313 & 0.9796 \(\pm\) 0.0029 & 0.8446 \(\pm\) 0.0047 \\ \cline{1-1}  & pedestrian & 1110 & 0.8572 \(\pm\) 0.0068 & 0.4812 \(\pm\) 0.0040 \\ \cline{1-1}  & person sitting & 73 & 0.7896 \(\pm\) 0.0377 & 0.4812 \(\pm\) 0.0135 \\ \cline{1-1}  & cyclist & 439 & 0.9290 \(\pm\) 0.0033 & 0.6788 \(\pm\) 0.0082 \\ \cline{1-1}  & tram & 157 & 0.9568 \(\pm\) 0.0040 & 0.7402 \(\pm\) 0.0073 \\ \cline{1-1}  & misc & 273 & 0.9540 \(\pm\) 0.0080 & 0.7380 \(\pm\) 0.0098 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Object detection performance on KITTI. The average \(\pm\) std value was obtained from five runs. The number of instances represents the number of bounding boxes for each class in the test set. In addition, () denotes our improvement over random selection.  
corresponding IoU showed the highest values. In contrast, the class with the least number of pixels, that is, the train class, achieved a performance of nearly zero. Apart from the wall, terrain, and rider classes, our selection methods outperformed the random methods. Particularly for the motorcycle class, we observed the highest improvement (+0.2831). We conjecture that the redundant training examples removed for random selection may include more motorcycles than that of our selection.