## 3 Algorithm Overview
Consequently, the representation is both of low-complexity and noise-robust. Test 2: In Test 2, the BTCM is tested for different pooling techniques (average, max and stochastic). The final outcomes have shown that the max pooling method is outperforming all existing methods. Figure 4 shows that the max pooling training accuracy is converging quickly as compared to other methods. The CSSA final version will be trained with the max pooling units. Test 3: Batch normalization layers offer us a higher accuracy and faster learning at the expense of additional computations and trainable parameters. This leads the learning process to become slower. To assess network performance and train it in a faster way, we have removed BN layers and try to train the network; though, the outcomes are not really encouraging. Figure 5 shows the final outcomes of the proposed model. The initial loss is too high, and it reduces down too slowly; whereas, the accuracy is improving slowly, and there is no considerable increase in it after a number of training iterations. According to [PERSON] (2016), besides a certain amount of computational complexity, there are out-weighed benefits attained by introducing the batch normalization unit. It is further mentioned that by reducing the batch size, we can efficiently reduce the batch normalization-associated overheads. It can also be achieved by sparsely distributing normalization layers throughout the CNN architecture. Therefore, the final version of CSSA is trained with sparse BN layers and a small batch size. We set the batch size limit to 12 to avoid BN-associated overheads. Test 4: To simplify the CNN pipeline, we propose Springenberg et al. 's [PERSON] et al. (2016) inspired BTCM and train it. The results are shown in Figure 6. It shows a very poor training outcome, where the accuracy remains close to zero, and loss stays higher than one. The outcomes are really discouraging, so this BTCM design is discarded from further analysis. The hypothesis to eliminate the pooling layer for the sake of network simplicity is not successful due to the whole network getting degraded through consecutive convolutions. Therefore, for the final CSSA experimentation, we eliminate the current BTCM and continue with max pooling units. Figure 4: Pooling methods’ accuracy curves. Test 5: The CSSA is a VGG-16-based model. Its architecture is shown in Table 1. It is composed of the best performing ELU activation function, the max pooling unit and BN layer. The aforementioned whole network is based on an encoder-decoder arrangement, which is trained end-to-end over the Pascal VOC-12 and CamVid datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Encoder**} & \multicolumn{2}{c}{**Decoder**} \\ \hline  & \multicolumn{2}{c}{**Input 360 \(\times\) 480 + Norm**} & \multicolumn{2}{c}{**Output, Soft-max-with-Loss, Accuracy**} \\ \hline Conv1 & 3 \(\times\) 3, 64 & BN, ELU, Pooling & De-Conv & 3 \(\times\) 3, 64 & Up-sample, BN, ELU \\  & 3 \(\times\) 3, 64 & & 3 \(\times\) 3, 64 & \\ \hline Conv2 & 3 \(\times\) 3, 128 & BN, ELU, Pooling & De-Conv & 3 \(\times\) 3, 128 & Up-sample, BN, ELU \\  & 3 \(\times\) 3, 128 & & 3 \(\times\) 3, 128 & \\ \hline Conv3 & 3 \(\times\) 3, 256 & BN, ELU, Pooling, Dropout & De-Conv & 3 \(\times\) 3, 256 & Up-sample, BN, ELU, Dropout \\  & 3 \(\times\) 3, 256 & & 3 \(\times\) 3, 256 & \\ \hline Conv4 & 3 \(\times\) 3, 512 & BN, ELU, Pooling, Dropout & De-Conv & 3 \(\times\) 3, 512 & Up-sample, BN, ELU, Dropout \\  & 3 \(\times\) 3, 512 & & 3 \(\times\) 3, 512 & \\ \hline Conv5 & 3 \(\times\) 3, 512 & BN, ELU, Pooling, Dropout & De-Conv & 3 \(\times\) 3, 512 & Up-sample, BN, ELU, Dropout \\  & 3 \(\times\) 3, 512 & & 3 \(\times\) 3, 512 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CSSA’s encoder and decoder layer architecture. Figure 5: CNN network results without Batch Normalization (BN) layers. Figure 6: Analysis of CNN by skipping pooling layers.