# CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions
## 1 Introduction  
Convolutional neural networks (CNNs) [1] have been widely applied in many fields, including image classification [2; 3; 4; 5; 6], natural language processing (NLP) [7], object detection [8; 9; 10; 11], and speech classification [12]. Many CNN models have been developed and improved, and they have been successfully applied in medical fields [13; 14], image denoising [15; 16; 17], and semantic segmentation [18; 19; 20]. The excellent performance of CNNs comes from their wider and deeper models [4]; however, these models have also faced an increasing memory burden [21], which limits their application in resource-constrained and high real-time requirement scenarios, such as mobile terminals and embedded systems with low hardware resources [22; 23]. The CNN operation usually extracts features through the convolutional layer and  
more contextual information [28]. The simple option is to stack successive convolutional layers or use a bigger size of filters, and the RF can be expanded, as mentioned in [29], but this often results in over-fitting because of large numbers of trainable parameters [13]. Some researchers [30; 31; 32] used various pruning methods to reduce the parameters, and these methods maintain or even improve the accuracy. There are many other methods to alleviate the over-fitting problem of CNNs, such as L2-regularization [33], dropout [34], and data augmentation. Data augmentation is commonly adopted to alleviate the over-fitting problem [35] and reduce the need for regularization [36]. Data augmentation methods include translation [37], horizontal flipping [38], vertical flip and rotation, etc. [PERSON] et al. [39] used full stage data augmentation in their model and achieved a better performance. Dilated convolution is a more effective method to expand the RF than the two methods mentioned above [15]. To reduce the computational complexity and improve the training speed, [PERSON] et al. [40] used dilated convolution to expand the size of the RF without sacrificing the resolution. Some researchers [11; 41] used dilated convolution for object detection. [PERSON] et al. [42; 43] used dilated convolution to effectively increase the receptive field in the source separation scheme, [PERSON] et al. [13] used dilated convolution for automatic calcium scoring, and [PERSON] et al. [14] used multi-scale dilated convolutions to extract richer features for computed tomography image segmentation. Besides exploiting dilated convolution in the network, many works employed transposed convolution (TC) operations to realize high-resolution predictions to avoid information loss. TC layers were used to recover the spatial resolution [19; 44]. [PERSON] et al. [45] used TC to recover low-resolution prediction maps. [PERSON] et al. [41] used TC to enrich low-level features and achieved superior performance in terms of the high detection rate. [PERSON] et al. [18] introduced TC for semantic segmentation. We present an architecture to fuse different features of standard convolution, dilated convolution, and TC (CDTNet) for image classification, which takes the VGG model [3] as the framework. The general idea of CDTNet is to capture richer information without increasing the number of parameters. We combined the standard and dilated convolution to extract multi-scale features and used transposed convolution to transmit features from low level to high level, which can recover part of the lost information in the pooling layers. Through the combination of these methods, the number of parameters can be decreased while reducing the loss value and improving the accuracy. To the best of our knowledge, we are the first to apply standard, dilated, and transposed convolutions together for image classification. We used three image datasets, CIFAR-10, SVHN, and FMNIST, to evaluate all models for convergence speed, loss, and accuracy. There are many popular image classification models, such as AlexNet, VGG, GoogLeNet, YOLO, ResNet, ResNeXt, DenseNet, and so on.