  There is plenty of theoretical and empirical evidence that depth of neural
networks is a crucial ingredient for their success. However, network training
becomes more difficult with increasing depth and training of very deep networks
remains an open problem. In this extended abstract, we introduce a new
architecture designed to ease gradient-based training of very deep networks. We
refer to networks with this architecture as highway networks, since they allow
unimpeded information flow across several layers on "information highways". The
architecture is characterized by the use of gating units which learn to
regulate the flow of information through a network. Highway networks with
hundreds of layers can be trained directly using stochastic gradient descent
and with a variety of activation functions, opening up the possibility of
studying extremely deep and efficient architectures.
