# An Efficient Dropout for Robust Deep Neural Networks
## 4 Dataset for Comparison
### Implementation Details
These results demonstrate the improved regularization and convergence stability provided by the proposed technique, particularly in deeper and more complex architectural contexts. To evaluate the consistency and robustness of the suggested dropout method, each of the four CNN models was trained five times under three distinct regularization settings: no dropout, standard dropout, and the proposed method. Table 4 summarizes the mean validation accuracies and accompanying standard deviations across various runs. For instance, in Model 1, the proposed approach produced a mean validation accuracy of 0.8002 \(\pm\) 0.0029, compared to 0.7852 \(\pm\) 0.0061 with traditional dropout and 0.7772 \(\pm\) 0.0041 with no dropout. Similar patterns were obtained in the more complex Model 4, where the proposed technique attained 0.5490 \(\pm\) 0.0050, significantly outperforming conventional dropout (0.5397 \(\pm\) 0.0049) and no dropout (0.5214 \(\pm\) 0.0068). These results reveal that the suggested technique not only enhances generalization performance but also exhibits lower variance over repeated runs, emphasizing its training stability and reliability. To assess the computational overhead of the proposed method, we compared training durations for Models 2 and 4 using standard dropout and the proposed method. For Model 2, the proposed method completed training approximately 11.6% faster than standard dropout over 20 epochs. Conversely, Model 4, which is a deeper ResNet-based architecture, experienced a roughly 23.3% increase in training time when using the proposed method. Despite the additional training time in deeper models, the proposed method consistently achieved higher validation accuracy and faster convergence in earlier epochs. This suggests that the performance gains justify the computational cost, particularly in complex architectures where effective regularization has a greater impact. To further evaluate the effectiveness of the proposed method, an additional experiment was run using the same dataset and network configuration described in a recent study by [PERSON] et al. (the Trainable Gradient Dropout technique) [11]. In this repeat study, the baseline network was trained without utilizing any dropout mechanism. Under these conditions, the model reached a peak validation accuracy of 73.11%, which aligns closely with the results presented in the original study. Using the identical network structure and dataset, the proposed dropout method was then performed. Remarkably, it achieved a validation accuracy of 74.48% within just 6 epochs, showing significantly faster convergence. Continued training led to a significant gain in performance, reaching 75.09% at epoch 63. These results show the better generalization and training effectiveness of the suggested approach compared to both the dropout-free baseline and the method given by [PERSON] et al. [11]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline No dropout & 0.7772 \(\pm\) 0.0041 & 0.5493 \(\pm\) 0.0043 & 0.5258 \(\pm\) 0.0063 & 0.5214 \(\pm\) 0.0068 \\ Standard & 0.7852 \(\pm\) 0.0061 & 0.5575 \(\pm\) 0.0032 & 0.5293 \(\pm\) 0.0055 & 0.5397 \(\pm\) 0.0049 \\ Dropout & 0.8002 \(\pm\) 0.0029 & 0.5821 \(\pm\) 0.0034 & 0.5408 \(\pm\) 0.0031 & 0.5490 \(\pm\) 0.0050 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean validation accuracy (\(\pm\)standard deviation) over five runs for each model and methods.