# Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles
## 3 Experiments
### Soft Parameter Sharing  
Additional experiments were conducted to confirm the effect of the regularization parameters of the deep multi-task learning. Our proposed model uses hard parameter sharing in which some parameters are shared between all tasks. On the other hand, in the soft parameter sharing, each task has its own model with corresponding parameters. The distance of some parameters of each model is regularized and trained similarly to each other by a regularization term. When this distance is zero, it is equivalent to hard parameter sharing. To examine the effect of regularization, the results of two single models, soft parameter sharing, and hard parameter sharing with ResNet architectures were compared. For soft parameter sharing, convolutional layers were regularized using L2 regularization. As shown in Table 9, soft parameter sharing has intermediate accuracy between two single models and hard parameter sharing. As shown in Table 10, in the task-specific accuracy, Task 1 shows high accuracy because of using many data. However, in the case of Task 2, the performance of the single-task model decreases as the layer goes deeper. On the other hand, the soft parameter sharing method using regularization maintains the performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Training Dataset**} & \multirow{3}{*}{**Methods**} & \multicolumn{5}{c}{**Architectures**} \\  & & \multicolumn{2}{c}{**ResNet-18**} & \multicolumn{2}{c}{**ResNet-50**} & \multicolumn{2}{c}{**ResNet101**} \\ \cline{3-8}  & & **Task 1** & **Task 2** & **Task 1** & **Task 2** & **Task 1** & **Task 2** \\  & & **(degree)** & **(cm)** & **(degree)** & **(cm)** & **(degree)** & **(cm)** \\ \hline Default & Single-task & 0.13 & 1.50 & 0.09 & 2.61 & 0.15 & 2.44 \\
270 & Single-task & (0.23) & (1.04) & (0.12) & (1.98) & (0.23) & (1.45) \\  & Multi-task & (0.10) & (0.52) & (0.12) & **0.50** & **0.06** & 0.66 \\  & & (0.11) & (0.24) & (0.14) & (**0.39**) & **(0.07**) & (0.51) \\ \hline Default + light on & Single-task & 0.04 & 1.37 & 0.12 & 2.28 & 0.20 & 2.32 \\
540 & Single-task & (0.09) & (1.00) & (0.16) & (1.93) & (0.43) & (1.87) \\  & Multi-task & 0.04 & 0.11 & 0.08 & 0.17 & **0.02** & **0.10** \\  & & (0.09) & (0.14) & (0.11) & (0.21) & **(0.05)** & **(0.17)** \\ \hline Default + flipped & Single-task & 0.38 & 4.64 & 0.40 & 5.22 & 0.51 & 6.40 \\
540 & Single-task & (0.38) & (3.43) & (0.31) & (1.57) & (0.27) & (2.97) \\  & Multi-task & **0.37** & **0.92** & 0.50 & 1.62 & 0.45 & 1.87 \\  & & **(0.21)** & **(0.40)** & (0.28) & (0.63) & (0.24) & (1.10) \\ \hline Default + light on + flipped & Single-task & 0.05 & 0.59 & 0.02 & 1.10 & 0.03 & 1.31 \\  & & (0.13) & (0.65) & (0.06) & (0.81) & (0.06) & (1.09) \\  & Multi-task & **0.01** & 0.09 & 0.02 & **0.07** & **0.01** & 0.09 \\  & & **(0.03)** & (0.09) & (0.04) & **(0.10)** & **(0.03)** & (0.13) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparisons of results using the different training datasets, in terms of mean absolute error. The best values for each architecture are in bold and the best performing model among the architectures is underlined.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Training Dataset**} & \multicolumn{2}{c}{**Architectures**} \\  & & **Number of Samples** & **ResNet-18** & **ResNet-50** & **ResNet-101** \\ \hline Single-task & & 0.88 & 0.80 & 0.81 \\  & Default + light on + flipped & (0.05) & (0.04) & (0.06) \\ Soft parameter sharing & Default + light on + flipped & 0.88 & 0.85 & 0.85 \\  & 1080 & (0.06) & (0.04) & (0.04) \\
**Hard parameter sharing** & & **0.95** & **0.95** & **0.96** \\
**(proposed)** & & **(0.03)** & **(0.03)** & **(0.03)** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparisons of results using the different methods of multi-task learning with three ResNet architectures, in terms of exact match ratio. The best values for each architecture are in bold.