IEA: Inner Ensemble Average within a convolutional neural network

Abduallah Mohamed, Xinrui Hua, Xianda Zhou, Christian Claudel

Abstract: Ensemble learning is a method of combining multiple trained models to improve model accuracy. We propose the usage of such methods, specifically ensemble average, inside Convolutional Neural Network (CNN) architectures by replacing the single convolutional layers with Inner Average Ensembles (IEA) of multiple convolutional layers. Empirical results on different benchmarking datasets show that CNN models using IEA outperform those with regular convolutional layers and advances the state of art. A visual and a similarity score analysis of the features generated from IEA explains why it boosts the model performance.

Keywords: Ensemble Convolutional Neural Networks

TL;DR: We inner ensemble the features of a convolutional neural layer, it increases the network accuracy and generates distinct features.

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [CIFAR-100](https://paperswithcode.com/dataset/cifar-100), [MNIST](https://paperswithcode.com/dataset/mnist)