# Extending Contrastive Learning to Unsupervised Redundancy Identification
## 7 Discussion  
As can be seen in the KITTI dataset, there could be a high probability of taking redundant examples in real world scenarios. Those examples carry similar visual context but exhibit a low level of contribution to performance. Given a fixed budget, avoiding redundant examples annotation is essential before the annotation process starts. Hence, our \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Pixel** & **Our** & **Random** \\
**Class** & **Count (\%)** & **Selection** & **Selection** \\ \hline all & 100 & 0.5614 \(\pm\) 0.0017 (+0.0381) & 0.5233 \(\pm\) 0.0018 \\ road & 24.76 & 0.9367 \(\pm\) 0.0002 (+0.0013) & 0.9654 \(\pm\) 0.0010 \\ sidewalk & 2.41 & 0.5963 \(\pm\) 0.0014 (+0.0028) & 0.5935 \(\pm\) 0.0050 \\ building & 17.13 & 0.8384 \(\pm\) 0.0003 (+0.0002) & 0.8382 \(\pm\) 0.0010 \\ wall & 0.40 & 0.2550 \(\pm\) 0.0177 (\(-\)0.0217) & 0.2767 \(\pm\) 0.0339 \\ fence & 0.97 & 0.4955 \(\pm\) 0.0007 (+0.0106) & 0.4849 \(\pm\) 0.0116 \\ pole & 1.18 & 0.4566 \(\pm\) 0.0026 (+0.0098) & 0.4468 \(\pm\) 0.0060 \\ light & 0.16 & 0.5111 \(\pm\) 0.0017 (+0.0096) & 0.5015 \(\pm\) 0.0026 \\ sign & 0.27 & 0.4700 \(\pm\) 0.0067 (+0.0007) & 0.4693 \(\pm\) 0.0079 \\ vegetation & 18.21 & 0.8571 \(\pm\) 0.0004 (+0.0009) & 0.8562 \(\pm\) 0.0003 \\ terrain & 1.07 & 0.4795 \(\pm\) 0.0020 (\(-\)0.0024) & 0.4819 \(\pm\) 0.0049 \\ sky & 20.62 & 0.9588 \(\pm\) 0.0005 (+0.0026) & 0.9562 \(\pm\) 0.0004 \\ person & 0.29 & 0.6557 \(\pm\) 0.0047 (+0.0048) & 0.6510 \(\pm\) 0.0051 \\ rider & 24.75 & 0.0000 \(\pm\) 0.0000 (\(-\)0.0024) & 0.0024 \(\pm\) 0.0042 \\ car & 10.54 & 0.8900 \(\pm\) 0.0005 (+0.0006) & 0.8895 \(\pm\) 0.0007 \\ truck & 1.19 & 0.5169 \(\pm\) 0.0069 (+0.0101) & 0.5068 \(\pm\) 0.0059 \\ bus & 0.73 & 0.7822 \(\pm\) 0.0004 (+0.0062) & 0.7760 \(\pm\) 0.0029 \\ train & 0.01 & 0.0000 \(\pm\) 0.0000 (+0.0000) & 0.0000 \(\pm\) 0.0000 \\ motorcycle & 0.03 & 0.4289 \(\pm\) 0.0133 (+0.2831) & 0.1458 \(\pm\) 0.2525 \\ bicycle & 0.02 & 0.5390 \(\pm\) 0.0297 (0.1795) & 0.3596 \(\pm\) 0.1779 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Semantic segmentation performance on BDD10K dataset. The average \(\pm\) std was obtained for the three runs. We measured the performance for each class using the IoU for all classes with the mIoU. In addition, we counted the pixels for each class in the test set in terms of percentage (second column). Notably, because pixels belonging to the training class exhibited a performance of nearly zero (0.01), both selection methods failed to capture the train. work can play the role of identifying redundant examples with the absence of annotations. However, there are limitations to our study, which can be directions for future research. * Although we proved that _cossim_ is appropriate for identifying a redundancy, the problem of defining a new metric better tailored to identifying redundancy should be addressed. The other future direction could be defining a new loss function, such that the DNN explicitly captures redundant examples. Our current work did not define the loss function but utilized a contrastive loss function. * For simplicity, we depended on the heuristic method through a human visual inspection to define redundant examples. However, the precise definition of redundancy could help derive a clear objective function. Finally, we hope that our study provides insight for future research on advanced methods for reducing redundant examples. J.J. methodology, software, conceptualization, writing--original draft, writing --review editing; H.J. review, editing;J.K. project administration, supervision, writing--review editing. All authors have read and agreed to the published version of the manuscript. This work was partly supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1C1C1007423). This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).