## 4 Experimental Results  
Prior to delving into the main body of this section, we will provide an elucidation of the factors that were applied to all our experiments. First of all, We applied the cutout technique for all experiments. Cutout [23] is an effective data preprocessing technique in Convolutional Neural Networks (CNN), which improves the generalization ability of the NAS algorithm. As other NAS algorithms sometimes apply cutout and sometimes do not, we denote +cutout with the model name in every table to indicate that cutout is applied. Secondly, the notion of search cost in the result tables refers to the quantity of tangible resources expended until the completion of a given search algorithm. This can be discerned through a comparative analysis of the duration taken by computations on various scales of computational units. We denoted GPU days to show the search cost. GPU day indicates the time taken for search when converted to one GPU. Since the GPU days can be varied to the GPU configurations, it may decrease as GPUs advance. Accordingly, an explanation of the device may be required. All of our experiments have been done with RTX A4000 and the results from the original DARTS paper have been done by the GTX 1080 Ti. When we ran the DARTS algorithm with RTX A4000, it took 2 days on average. Since the time to calculate the temperature for every epoch in our method takes only a few seconds, the search cost is nearly the same. Lastly, Params in the result tables indicates the number of parameters that the model contains. This is often referred to as the size of the model. Table 1 shows the results obtained by searching with different temperatures. The CIFAR-10 dataset was used, and the search was conducted for 50 epochs. Search validation accuracy represents the validation accuracy of the last epoch. Train top 1 accuracy refers to the top 1 accuracy of the last epoch when retraining with the searched architecture. Retraining was performed for 600 epochs. Since all top 5 accuracies showed results above 99.99%, the difference cannot be determined by the second decimal place alone. Therefore, we did not indicate the top 5 accuracies. Test accuracy represents the test accuracy of the retrained model. As there are infinitely many possible temperatures, only a few temperatures were selected for experimentation. In Table 1, the temperature with the highest test accuracy was 3.0, recording 97.40%. The second highest temperature was 0.1, which recorded 97.39%, 0.01%p lower than the accuracy at temperature 3.0. The temperature with the highest validation accuracy was 0.5, recording 88.90%. The second highest validation accuracy was achieved at temperature 0.1, recording 88.74%. Temperature 0.1, which showed high rankings in both test and validation accuracy, appears to be the most suitable temperature for the CIFAR-10 dataset among these examined temperatures. Empirically, architectures with high validation accuracy have a higher chance of improved performance after retraining. Therefore, if multiple trials are conducted, the temperature value of 0.1 may surpass the temperature value of 10.0. However, this is not always the case. For example, in the case of the architecture with a temperature of 0.5, despite recording the highest validation accuracy, it showed the lowest performance in terms of test accuracy. Table 2 shows the results of experiments conducted on the CIFAR-100 dataset. The items are similar to the Table 2, comparing search validation accuracy, train top 1 accuracy, test accuracy, and additionally train top 5 accuracies. In the CIFAR-100 dataset experiment, a temperature of 0.5 showed the highest performance in all categories, and the test accuracy recorded was 81.67%. It can be observed that the performance significantly dropped at temperatures 0.1 and 5.0. The test accuracy at temperature 0.1 was 76.86%, a decrease of 4.81%p compared to temperature 0.5. At temperature 5.0, the test accuracy was 78.80%, a decrease of 2.87%p compared to temperature 0.5.