An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching

Rene Schuster, Oliver Wasenmuller, Christian Unger, Didier Stricker; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Training a deep neural network is a non-trivial task. Not only the tuning of hyperparameters, but also the gathering and selection of training data, the design of the loss function, and the construction of training schedules is important to get the most out of a model. In this study, we perform a set of experiments all related to these issues. The model for which different training strategies are investigated is the recently presented SDC descriptor network (stacked dilated convolution). It is used to describe images on pixel-level for dense matching tasks. Our work analyzes SDC in more detail, validates some best practices for training deep neural networks, and provides insights into training with multiple domain data.

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[bibtex]
@InProceedings{Schuster_2019_CVPR_Workshops,
author = {Schuster, Rene and Wasenmuller, Oliver and Unger, Christian and Stricker, Didier},
title = {An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}