@inproceedings{1d26b35bad06484b8aec2665d29f5883,
title = "Direct: Deep discriminative embedding for clustering of ligo data",
abstract = "In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks. The trained embedding function transfers knowledge from the domain of a labeled set of morphologically-distinct images, known as classes, to a new domain within which new classes can potentially be isolated and identified. Our target application in this paper is the Gravity Spy Project, which is an effort to characterize transient, non-Gaussian noise present in data from the Advanced Laser Interferometer Gravitational-wave Observatory, or LIGO. Accumulating large, labeled sets of noise features and identifying of new classes of noise lead to a better understanding of their origin, which makes their removal from the data and/or detectors possible.",
keywords = "Deep Learning, Domain adaptation, Image Clustering, LIGO",
author = "S. Bahaadini and N. Rohani and Katsaggelos, {A. K.} and V. Noroozi and S. Coughlin and M. Zevin",
note = "Funding Information: This work was supported in part by an NSF INSPIRE grant (award number IIS-1547880) and IDEAS Data Science Fellowship, supported by the National Science Foundation under grant DGE-1450006.; 25th IEEE International Conference on Image Processing, ICIP 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/ICIP.2018.8451708",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "748--752",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
address = "United States",
}