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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

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

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    Date Created
    • 2018
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    • Doctoral Dissertation Electrical Engineering 2018

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