## 4 Experimental Results
This indicates that the relationship between temperature and performance is not directly proportional or inversely proportional. Finding and applying the appropriate temperature value is crucial. Table 3 presents the validation results during the architecture search process. Both Validation accuracy and Test accuracy refer to Top-1 accuracy. The validation accuracy in Table 3 represents the average result obtained after five experiments. As seen in Table 3, the validation accuracy during the search process is 88.35% for DARTS and 88.42% for TA-DARTS, with a temperature setting of 0.1. This indicates an increase in accuracy of 0.07% compared to the original DARTS algorithm. Table 4 shows the test results after retraining with the searched architecture. With a temperature value of 0.5, it achieved a top-1 accuracy of 97.40%, which is 0.16%p higher than the original DARTS mixed operator and 0.03%p higher than the Gumbel-softmax method. In addition, Different temperature values can be explored for future research, and a Hyperparameter Optimization (HPO) algorithm can be developed to find the optimal temperature value. Another approach could involve studying the correlation between temperature and loss to find the optimal value. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & **Search Validation** & **Train Top 1** & **Test** \\  & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline TA-DARTS (T = 0.1) + cutout & 88.74 & 99.14 & 97.39 \\ TA-DARTS (T = 0.5) + cutout & 88.90 & 99.85 & 95.25 \\ TA-DARTS (T = 0.8) + cutout & 88.38 & 98.62 & 96.93 \\ TA-DARTS (T = 2.0) + cutout & 88.34 & 98.58 & 97.20 \\ TA-DARTS (T = 3.0) + cutout & 88.53 & 99.09 & 97.40 \\ TA-DARTS (T = 10.0) + cutout & 87.87 & 99.01 & 97.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results on CIFAR-10 dataset with different temperatures. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Search Validation** & **Train Top 1** & **Test** \\  & **Validation** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline TA-DARTS (T = 0.1) + cutout & 63.14 & 92.95 & 99.37 & 76.86 \\ TA-DARTS (T = 0.5) + cutout & 64.12 & 97.84 & 99.89 & 81.67 \\ TA-DARTS (T = 5.0) + cutout & 63.65 & 93.71 & 99.38 & 78.80 \\ TA-DARTS (T = 10.0) + cutout & 63.40 & 95.91 & 99.69 & 81.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results on CIFAR-100 dataset with different temperatures. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Params (M)** & **Search Cost** & **Validation** & **Search Method** \\  & **(GPU days)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline DARTS (2 nd order) + cutout [10] & 3.4 & 1 & 88.35 \(\pm\) 0.19 & Gradient-based \\ TA-DARTS (T = 0.1) + cutout & 4.1 & 4 & 88.42 \(\pm\) 0.28 & Gradient-based \\ TA-DARTS (T = 10) + cutout & 4.1 & 4 & 87.98 \(\pm\) 0.20 & Gradient-based \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of validation accuracy with similar gradient-based approaches on CIFAR-10 dataset. The validation accuracies are obtained after five times of search. DARTS adopts a cell-based neural architecture [10; 24; 25; 26]. One distinctive feature of the cell-based architecture is that it incorporates a neural architecture within a single building block called a cell [6; 8; 27]. All cells share the same structure, and their weights are updated simultaneously [8]. In DARTS' cell-based architecture, the output of the last cell and the output of the cell before it are used as the input for the next cell. Each cell consists of multiple layers. In the search architecture of TA-DARTS, a cell comprises eight layers connected to a total of five nodes, and each layer is equipped with a mixed operator. Figures depicting the architectural variations under temperature values of 0.1 and 10 can be found in the Appendix section.