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Deep Active Learning Explored Across Diverse Label Spaces

Abstract Deep learning architectures have been widely explored in computer vision and have

depicted commendable performance in a variety of applications. A fundamental challenge

in training deep networks is the requirement of large amounts of labeled training

data. While gathering large quantities of unlabeled data is cheap and easy, annotating

the data is an expensive process in terms of time, labor and human expertise.

Thus, developing algorithms that minimize the human effort in training deep models

is of immense practical importance. Active learning algorithms automatically identify

salient and exemplar samples from large amounts of unlabeled data and can augment

maximal information to supervised learning models, thereby reducing the hum... (more)
Created Date 2018
Contributor Ranganathan, Hiranmayi (Author) / Sethuraman, Panchanathan (Advisor) / Papandreou-Suppappola, Antonia (Committee member) / Li, Baoxin (Committee member) / Chakraborty, Shayok (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Computer science / Active Learning / Deep Learning / Multiclass / Multilabel / Multimodal / Regression
Type Doctoral Dissertation
Extent 247 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Electrical Engineering 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis