  Deep convolutional neural networks (CNNs) have dominated many computer vision
domains because of their great power to extract good features automatically.
However, many deep CNNs-based computer vison tasks suffer from lack of training
data while there are millions of parameters in the deep models. Obviously,
these two biphase violation facts will result in parameter redundancy of many
poorly designed deep CNNs. Therefore, we look deep into the existing CNNs and
find that the redundancy of network parameters comes from the correlation
between features in different channels within a convolutional layer. To solve
this problem, we propose the stochastic channel decorrelation (SCD) block
which, in every iteration, randomly selects multiple pairs of channels within a
convolutional layer and calculates their normalized cross correlation (NCC).
Then a squared max-margin loss is proposed as the objective of SCD to suppress
correlation and keep diversity between channels explicitly. The proposed SCD is
very flexible and can be applied to any current existing CNN models simply.
Based on the SCD and the Fully-Convolutional Siamese Networks, we proposed a
visual tracking algorithm to verify the effectiveness of SCD.
