Free energy of Bayesian Convolutional Neural Network with Skip
  Connection
Shuya Nagayasu Sumio Watanabe
Since the success of Residual Network(ResNet), many of architectures of
Convolutional Neural Networks(CNNs) have adopted skip connection. While the
generalization performance of CNN with skip connection has been explained
within the framework of Ensemble Learning, the dependency on the number of
parameters have not been revealed. In this paper, we show that Bayesian free
energy of Convolutional Neural Network both with and without skip connection in
Bayesian learning. The upper bound of free energy of Bayesian CNN with skip
connection does not depend on the oveparametrization and, the generalization
error of Bayesian CNN has similar property.