# Inception Recurrent Convolutional Neural Network for Object Recognition

arXiv: https://arxiv.org/abs/1704.07709 · HTML: https://mlsanity.com/paper/1704.07709/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation](https://mlsanity.com/paper/2209.06107/) (2022)
- [Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms](https://mlsanity.com/paper/2209.00735/) (2022)
- [Online Continual Learning via the Knowledge Invariant and Spread-out Properties](https://mlsanity.com/paper/2302.00858/) (2023)
- [Pruning Distorted Images in MNIST Handwritten Digits](https://mlsanity.com/paper/2307.14343/) (2023)
- [Recurrent Joint Embedding Predictive Architecture with Recurrent Forward Propagation Learning](https://mlsanity.com/paper/2411.16695/) (2024)

## Abstract (arXiv)

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.