  How to reduce the requirement on training dataset size is a hot topic in deep
learning community. One straightforward way is to reuse some pre-trained
parameters. Some previous work like Deep transfer learning reuse the model
parameters trained for the first task as the starting point for the second
task, and semi-supervised learning is trained upon a combination of labeled and
unlabeled data. However, the fundamental reason of the success of these methods
is unclear. In this paper, the reusability of parameters in each layer of a
deep convolutional neural network is experimentally quantified by using a
network to do segmentation and auto-encoder task. This paper proves that
network parameters can be reused for two reasons: first, the network features
are general; Second, there is little difference between the pre-trained
parameters and the ideal network parameters. Through the use of parameter
replacement and comparison, we demonstrate that reusability is different in
BN(Batch Normalization)[7] layer and Convolution layer and some observations:
(1)Running mean and running variance plays an important role than Weight and
Bias in BN layer.(2)The weight and bias can be reused in BN layers.( 3) The
network is very sensitive to the weight of convolutional layer.(4) The bias in
Convolution layers are not sensitive, and it can be reused directly.
