  Traditional self-attention mechanisms in convolutional networks tend to use
only the output of the previous layer as input to the attention network, such
as SENet, CBAM, etc. In this paper, we propose a new attention modification
method that tries to get the output of the classification network in advance
and use it as a part of the input of the attention network. We used the
auxiliary classifier proposed in GoogLeNet to obtain the results in advance and
pass them into attention networks. we added this mechanism to SE-ResNet for our
experiments and achieved a classification accuracy improvement of at most 1.94%
on cifar100.
