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Trade-offs Between Inference and Learning in Image Segmentation
Author(s): Quan Nguyen; Reid Porter; Beate Zimmer
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Paper Abstract

During end-to-end learning, application level performance metrics, in combination with large training sets, are used to optimize deep neural network pipelines for the task at hand. There are two main places where application level performance metrics are typically introduced: in energy functions that are minimized during inference and in loss functions that are minimized during training. Minimizing energy functions and minimizing loss functions are both hard problems in the general case and an application specific trade-off must be made between how much effort is spent in inference versus training. In this paper we explore this trade-off in the context of image segmentation. Specifically, we use a novel, computationally efficient, family of networks to investigate the trade-off between two traditional extremes. At one extreme are inference networks that minimize a correlation clustering energy function. At the other extreme are learning networks that minimize a Rand Error loss function.

Paper Details

Date Published: 6 September 2019
PDF: 8 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390P (6 September 2019); doi: 10.1117/12.2526825
Show Author Affiliations
Quan Nguyen, Texas A&M Univ. Corpus Christi (United States)
Reid Porter, Los Alamos National Lab. (United States)
Beate Zimmer, Texas A&M Univ. Corpus Christi (United States)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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