  Improving information flow in deep networks helps to ease the training
difficulties and utilize parameters more efficiently. Here we propose a new
convolutional neural network architecture with alternately updated clique
(CliqueNet). In contrast to prior networks, there are both forward and backward
connections between any two layers in the same block. The layers are
constructed as a loop and are updated alternately. The CliqueNet has some
unique properties. For each layer, it is both the input and output of any other
layer in the same block, so that the information flow among layers is
maximized. During propagation, the newly updated layers are concatenated to
re-update previously updated layer, and parameters are reused for multiple
times. This recurrent feedback structure is able to bring higher level visual
information back to refine low-level filters and achieve spatial attention. We
analyze the features generated at different stages and observe that using
refined features leads to a better result. We adopt a multi-scale feature
strategy that effectively avoids the progressive growth of parameters.
Experiments on image recognition datasets including CIFAR-10, CIFAR-100, SVHN
and ImageNet show that our proposed models achieve the state-of-the-art
performance with fewer parameters.
