With the expansion and success of convolutional networks, the topic of deep learning has attracted increasing attention in recent years; Since convolutional networks include many layers, optimal learning of network layers is of great importance. In this paper, a new mod
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With the expansion and success of convolutional networks, the topic of deep learning has attracted increasing attention in recent years; Since convolutional networks include many layers, optimal learning of network layers is of great importance. In this paper, a new model, called the 4-stream model, is presented with the aim of helping to linearize the data space using representational dissimilarity transformation, and the effects of this transformation on standard classifications for artificial data and Cifar10 images are investigated. Then, two models based on data preprocessing with dissimilarity transform representation and Sobel and Edge Detector filters are analyzed. The 4-stream model increased the accuracy by 3.2% due to the increase in the number of model parameters, and hence the capacity of the network. Besides, adding the dissimilarity representation wherever the classifier cannot perform a high-resolution classification by merely using the main features, can help to increase the discriminability of classes by adding linear features.
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