  Residual networks, which use a residual unit to supplement the identity
mappings, enable very deep convolutional architecture to operate well, however,
the residual architecture has been proved to be diverse and redundant, which
may leads to low-efficient modeling. In this work, we propose a competitive
squeeze-excitation (SE) mechanism for the residual network. Re-scaling the
value for each channel in this structure will be determined by the residual and
identity mappings jointly, and this design enables us to expand the meaning of
channel relationship modeling in residual blocks. Modeling of the competition
between residual and identity mappings cause the identity flow to control the
complement of the residual feature maps for itself. Furthermore, we design a
novel inner-imaging competitive SE block to shrink the consumption and re-image
the global features of intermediate network structure, by using the
inner-imaging mechanism, we can model the channel-wise relations with
convolution in spatial. We carry out experiments on the CIFAR, SVHN, and
ImageNet datasets, and the proposed method can challenge state-of-the-art
results.
