1 code implementation • 18 Feb 2021 • Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov
We address the problem of learning of continuous exponential family distributions with unbounded support.
no code implementations • 18 May 2020 • Christopher X. Ren, Amanda Ziemann, James Theiler, Alice M. S. Durieux
In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in remote sensing imagery, even in an unpaired training setting.
no code implementations • 7 Dec 2019 • Hannah J. Murphy, Christopher X. Ren, Matthew T. Calef
Anomalous change detection (ACD) methods separate common, uninteresting changes from rare, significant changes in co-registered images collected at different points in time.
no code implementations • 28 Nov 2019 • Christopher X. Ren, Amanda Ziemann, Alice M. S. Durieux, James Theiler
This paper introduces a new method of generating realistic pervasive changes in the context of evaluating the effectiveness of change detection algorithms in controlled settings.