Wide deep residual networks in networks

• Published in: Multimedia tools and applications 2023-02, Vol.82 (5), p.7889-7899
• Main Authors: Alaeddine, Hmidi, Jihene, Malek
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Linear filters; Model accuracy; Multilayer perceptrons; Multimedia Information Systems; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia; Training
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-13696-0

The Deep Residual Network in Network (DrNIN) model [ 18 ] is an important extension of the convolutional neural network (CNN). They have proven capable of scaling up to dozens of layers. This model exploits a nonlinear function, to replace linear filter, for the convolution represented in the layers of multilayer perceptron (MLP) [ 23 ]. Increasing the depth of DrNIN can contribute to improved classification and detection accuracy. However, training the deep model becomes more difficult, the training time slows down, and a problem of decreasing feature reuse arises. To address these issues, in this paper, we conduct a detailed experimental study on the architecture of DrMLPconv blocks, based on which we present a new model that represents a wider model of DrNIN. In this model, we increase the width of the DrNINs and decrease the depth. We call the result module (WDrNIN). On the CIFAR-10 dataset, we will provide an experimental study showing that WDrNIN models can gain accuracy through increased width. Moreover, we demonstrate that even a single WDrNIN outperforms all network-based models in MLPconv network models in accuracy and efficiency with an accuracy equivalent to 93.553% for WDrNIN-4-2.