# Inner-Imaging Networks: Put Lenses into Convolutional Structure

## TL;DR

This paper introduces Inner-Imaging networks that reorganize channel relationships within convolutional layers by mapping channels onto a pseudo-image, enhancing diversity, complementarity, and efficiency in deep CNNs.

## Contribution

The paper proposes a novel Inner-Imaging architecture that models intra- and inter-channel relationships by organizing channels into groups and mapping them onto a pseudo-image, improving CNN performance.

## Key findings

- Improved accuracy on CIFAR, SVHN, and ImageNet datasets.
- Enhanced channel diversity and complementarity.
- Lightweight and easy-to-implement architecture.

## Abstract

Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address this issue by enhancing diversities of filters, they have not considered the complementarity and the completeness of the internal structure of the convolutional network. To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intra-group and inter-group relationships simultaneously. The convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudo-image, like putting a lens into convolution internal structure. Consequently, not only the diversity of channels is increased, but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implemented. It provides an efficient self-organization strategy for convolutional networks so as to improve their efficiency and performance. Extensive experiments are conducted on multiple benchmark image recognition data sets including CIFAR, SVHN and ImageNet. Experimental results verify the effectiveness of the Inner-Imaging mechanism with the most popular convolutional networks as the backbones.