  Machine learning and computer vision have driven many of the greatest
advances in the modeling of Deep Convolutional Neural Networks (DCNNs).
Nowadays, most of the research has been focused on improving recognition
accuracy with better DCNN models and learning approaches. The recurrent
convolutional approach is not applied very much, other than in a few DCNN
architectures. On the other hand, Inception-v4 and Residual networks have
promptly become popular among computer the vision community. In this paper, we
introduce a new DCNN model called the Inception Recurrent Residual
Convolutional Neural Network (IRRCNN), which utilizes the power of the
Recurrent Convolutional Neural Network (RCNN), the Inception network, and the
Residual network. This approach improves the recognition accuracy of the
Inception-residual network with same number of network parameters. In addition,
this proposed architecture generalizes the Inception network, the RCNN, and the
Residual network with significantly improved training accuracy. We have
empirically evaluated the performance of the IRRCNN model on different
benchmarks including CIFAR-10, CIFAR-100, TinyImageNet-200, and CU3D-100. The
experimental results show higher recognition accuracy against most of the
popular DCNN models including the RCNN. We have also investigated the
performance of the IRRCNN approach against the Equivalent Inception Network
(EIN) and the Equivalent Inception Residual Network (EIRN) counterpart on the
CIFAR-100 dataset. We report around 4.53%, 4.49% and 3.56% improvement in
classification accuracy compared with the RCNN, EIN, and EIRN on the CIFAR-100
dataset respectively. Furthermore, the experiment has been conducted on the
TinyImageNet-200 and CU3D-100 datasets where the IRRCNN provides better testing
accuracy compared to the Inception Recurrent CNN (IRCNN), the EIN, and the
EIRN.
