Low-Cost Transfer Learning of Face Tasks

John, Trupthi Ann and Dua, Isha and Balasubramanian, Vineeth N and C V, Jawahar (2019) Low-Cost Transfer Learning of Face Tasks. arXiv. pp. 1-13. (Submitted)

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Do we know what the different filters of a face network represent? Can we use this filter information to train other tasks without transfer learning? For instance, can age, head pose, emotion and other face related tasks be learned from face recognition network without transfer learning? Understanding the role of these filters allows us to transfer knowledge across tasks and take advantage of large data sets in related tasks. Given a pretrained network, we can infer which tasks the network generalizes for and the best way to transfer the information to a new task. We demonstrate a computationally inexpensive algorithm to reuse the filters of a face network for a task it was not trained for. Our analysis proves these attributes can be extracted with an accuracy comparable to what is obtained with transfer learning, but 10 times faster. We show that the information about other tasks is present in relatively small number of filters. We use these insights to do task specific pruning of a pretrained network. Our method gives significant compression ratios with reduction in size of 95% and computational reduction of 60%

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Article
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 28 Jan 2019 06:16
Last Modified: 28 Jan 2019 06:16
URI: http://raiith.iith.ac.in/id/eprint/4763
Publisher URL:
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