# CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions
## 3 Fuse Different Features of CDTNet  
Inspired by the previously mentioned research, we proposed CDTNet to fuse different features of standard, dilated, and transposed convolutions for image classification. Similar to the VGG models, we used 3 \(\times\) 3 filters and doubled the filter numbers after every pooling operation [3], except the last one. In our model, we used two parallel convolution operations in the beginning stage: one uses standard convolution and the other uses dilated convolution. The results of the two branches are fused by a concatenation operation, which naturally raises the dimension. After the concatenating operation, we used a block containing 1 \(\times\) 1 convolution, Batch Normalization (BN) [70], ReLU [71], and 2 \(\times\) 2 max-pooling (CBRP), four consecutive operations, to reduce the channel number of the feature map. The process of standard and dilated convolutions is shown in Figure 3.  
The CDTNet comprises five blocks in Figure 3, and each block contains three parts: standard and dilated convolutions, concatenate, and CBRP operations. The usage of standard and dilated convolution layers can capture image clues of multiple scales by expanding the RF and can avoid increasing the number of model parameters. In each block, rate = 1 represents standard convolution, and rate = 2 represents that the dilation rate is 2 in the dilated convolution operation. The concatenation operation can combine the features from two branches, which represent the features of the global and local images. After each concatenation operation, we added a CBRP operation to extract features. The size of the convolutional kernel is 1 \(\times\) 1, which has been used to reduce the parameters of the network and computational costs. In addition, the third dimension of feature maps, i.e., the number of channels, is controlled by the number of the 1 \(\times\) 1 filter. It is useful to increase the dilation rate moderately for better performance [61]. [PERSON] et al. [14] used four dilated rates to extract features, then fused the four levels of features to make full use of the low-level and high-level features. Larger RF can be obtained by enlarging the dilation rate; however, as the filling size increases with the dilation rate, the  
Figure 3: Process of standard and dilated convolution. boundary effect is introduced, which counterbalances the effect of large RF obtained by increasing the dilation rate [61]. We used two dilation rates in CDTNet, i.e., rate = 1 and rate = 2. There are five blocks with the process of standard and dilated convolutions, and the output size of each block is half the size of the front block's output. The output of each block is fed into up-sampling units for finer information recovery, which can generate high-resolution features. The compared experiments of [61] suggest that the features extracted with a small upscaling factor could retain more detailed information. Thus, we used the filter number in the block: divide by 2 for \(\times\)2 TC, divide by 4 for \(\times\)4 TC, and so on. The last two TCs have the same filter number. All TC results of the same size are concatenated together with the same size pooling result; the process is shown in Figure 4.  
In Figure 4, pool1 to pool5 correspond to pool1 to pool5 and block1 to block5 correspond to block1 to block5 in Figure 3. The polylines with an arrow represent TC operations, which are marked with \"TC\", and the straight lines under each pool block without arrows represent skip connections, which are marked with \"copy\". The pooling operation will abandon some important feature information. The skip connection is widely used in many popular deep networks, and the advantage is that it allows more lower-level information to reach the top level. We used skip connection in CDTNet, and the fused feature map retains the high resolution of the lower-level feature map and represents better semantic information. In Figure 4, the feature maps in previous layers are fused with other TC results.