# Inception Recurrent Convolutional Neural Network for Object Recognition

## TL;DR

This paper introduces the Inception Recurrent Convolutional Neural Network (IRCNN), a novel deep learning architecture that combines inception modules with recurrent layers, demonstrating improved recognition accuracy on multiple benchmark datasets.

## Contribution

The paper presents a new IRCNN model that integrates inception and recurrent layers, showing superior performance over existing DCNN architectures on standard datasets.

## Key findings

- IRCNN achieves comparable or higher accuracy than existing DCNNs.
- IRCNN outperforms RCNN, Inception, and Inception-Residual Networks on CIFAR-100.
- IRCNN shows 2.54% to 3.5% improvement in classification accuracy.

## Abstract

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.