Semantic Segmentation using Neural Ordinary Differential Equations

• URL: http://arxiv.org/abs/2209.08667v1
• Date: Sun, 18 Sep 2022 22:13:55 GMT
• Title: Semantic Segmentation using Neural Ordinary Differential Equations
• Authors: Seyedalireza Khoshsirat, Chandra Kambhamettu
• Abstract summary: 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. 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.
• Score: 3.7588109000722727
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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