Inception Recurrent Convolutional Neural Network for Object Recognition
This work addresses object recognition for computer vision applications, presenting an incremental improvement over existing deep learning architectures.
The paper tackles object recognition by proposing an Inception-Recurrent Convolutional Neural Network (IRCNN), which combines inception and recurrent layers, and reports improvements of up to 3.5% in classification accuracy on benchmark datasets like CIFAR-100 compared to existing models.
Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a new deep learning model called an Inception- Recurrent Convolutional Neural Network (IRCNN), which utilizes the power of an inception network combined with recurrent layers in DCNN architecture. We have empirically evaluated the recognition performance of the proposed IRCNN model using different benchmark datasets such as MNIST, CIFAR-10, CIFAR- 100, and SVHN. Experimental results show similar or higher recognition accuracy when compared to most of the popular DCNNs including the RCNN. Furthermore, we have investigated IRCNN performance against equivalent Inception Networks and Inception-Residual Networks using the CIFAR-100 dataset. We report about 3.5%, 3.47% and 2.54% improvement in classification accuracy when compared to the RCNN, equivalent Inception Networks, and Inception- Residual Networks on the augmented CIFAR- 100 dataset respectively.