  The idea of neural Ordinary Differential Equations (ODE) is to approximate
the derivative of a function (data model) instead of the function itself. In
residual networks, instead of having a discrete sequence of hidden layers, the
derivative of the continuous dynamics of hidden state can be parameterized by
an ODE. It has been shown that this type of neural network is able to produce
the same results as an equivalent residual network for image classification. In
this paper, we design a novel neural ODE for the semantic segmentation task. We
start by a baseline network that consists of residual modules, then we use the
modules to build our neural ODE network. We show that our neural ODE is able to
achieve the state-of-the-art results using 57% less memory for training, 42%
less memory for testing, and 68% less number of parameters. We evaluate our
model on the Cityscapes, CamVid, LIP, and PASCAL-Context datasets.
