## 4 Experiments and Results  
We make some ablation analysis to prove the advantages of each proposed module in MM-CNN. To make the comparison fair, we use the average and standard deviation of 10 checkpoints after convergence to measure the performance of the model. For our self-made sensing array, there are two optical kinds of input. The first one is the 8*8 matrix which loses the spatial information and the other is the \(12\times 12\) matrix which recovers the spatial distribution of sensing units. We compare two inputs on the baseline structure and the structure with the global pooling layer. As shown in Table 2, we find that the input with spatial information performs better. In addition, although the structure with the global pooling layer cannot perform as well as the baseline model on the GSD, we decide to use it as it can process the input of different sizes. More details about the influence of the global pooling layer will be discussed in the following paragraphs. In general, we use the input with spatial information as the input of self-made sensing array in the following experiments. We train models on train sets to compare the influence of the global pooling layer on different test sets. We resize the input to the unified size of \(16\times 12\) by padding zeros in some experiments. In this way, the baseline model which does not include the global pooling layer can be also trained with the combination of GKD and GSD. On the one hand, this operation has little influence on the results of the experiments. On the other hand, we also pad them into the fixed size of \(16\times 12\) in cases where the padding operation is not necessary, to make the comparison fair. The results are shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Input Size**} & **Avg \(\pm\) Std** & **Highest Accuracy** \\  & & **Accuracy on GSD** & **on GSD** \\ \hline CNN baseline & \(8\times 8\) & \(94.59\%\pm 0.50\%\) & 95.29\% \\ \hline CNN baseline & \(12\times 12\) & \(95.45\%\pm 0.34\%\) & 95.98\% \\ \hline CNN baseline + global pooling & \(8\times 8\) & \(93.67\%\pm 0.76\%\) & 94.36\% \\ \hline CNN baseline + global pooling & \(12\times 12\) & \(93.99\%\pm 0.54\%\) & 94.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of structures trained on the data w/o spatial information. We find that deep learning models without global pooling which are trained with the GKD and GSD do not perform well when applying the models of one sensor to the inference of the other unseen dataset and the accuracies are lower than 70%. Additionally, the performances on the other sensor increase at least 10% when we add the global pooling layer. For the evaluation on the GSD, it even increases from 69.56% to 85.14%. Accuracies are better when we let the model train on the GMD and then evaluate it. If we do not apply the operation of padding zero to the fixed size, we just utilize the model of baseline structure with the global pooling layer and train it with the GMD. In this case, the model comes out with the best checkpoint accuracy of (99.49%, 94.02%) on the test set of GDK and GSD. While it still has the space for improvement. From this part, two fully-connected heads are applied in our experiments corresponding to two datasets of different sensing arrays. As mentioned before, we pretrain the tactile representation and one fully-connected head on one dataset and then freeze them. Then, we finetune the other head with the other dataset to get the whole model. Results in Table 4 show that the order of dataset does not influence a lot and it comes out with the best checkpoint accuracy of (99.49%, 94.25%) on the test set of GDK and GSD. It outperforms the single head structure as the dependence in each head is sufficient to maintain the classification accuracy. Additionally, it also shows that it is available to utilize the tactile representation with the weight sharing mechanism. In addition, we design the mask branch to predict which head to use during inference.