Stacked generative adversarial networks for learning additional features of image segmentation maps
Date
Authors
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.
History
The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.
Dates of Existence
1969-present
Related Units
- College of Liberal Arts and Sciences (parent college)
Journal Issue
Is Version Of
Versions
Series
Department
Abstract
It has been shown that image segmentation models can be improved with an adversarial loss. Additionally, previous analysis of adversarial examples in image classification has shown that image datasets contain features that are not easily recognized by humans. This work investigates the effect of using a second adversarial loss to further improve image segmentation. The proposed model uses two generative adversarial networks stacked together, where the first generator takes an image as input and generates a segmentation map. The second generator then takes this predicted segmentation map as input and predicts the errors relative to the ground truth segmentation map. If these errors contained additional features that are not easily recognized by humans, they could possibly be learned by a discriminator. The proposed model did not consistently show significant improvement over a single generative adversarial model, casting doubt about the existence of such features.