  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.
