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Transfer learning across human activities using a cascade neural network architecture

Transfer learning across human activities using a cascade neural network architecture
Transfer learning across human activities using a cascade neural network architecture
Cascade Learning (CL) [20] is a new adaptive approach to train deep neural networks. It is particularly suited to transfer learning, as learning is achieved in a layerwise fashion, enabling the transfer of selected layers to optimize the quality of transferred features. In the domain of Human Activity Recognition (HAR), where the consideration of resource consumption is critical, CL is of particular interest as it has demonstrated the ability to achieve significant reductions in computational and memory costs with negligible performance loss. In this paper, we evaluate the use of CL and compare it to end to end (E2E) learning in various transfer learning experiments, all applied to HAR. We consider transfer learning across objectives, for example opening the door features transferred to opening the dishwasher. We additionally consider transfer across sensor locations on the body, as well as across datasets. Over all of our experiments, we find that CL achieves state of the art performance for transfer learning in comparison to previously published work, improving F1 scores by over 15%. In comparison to E2E learning, CL performs similarly considering F1 scores, with the additional advantage of requiring fewer parameters. Finally, the overall results considering HAR classification performance and memory requirements demonstrate that CL is a good approach for transfer learning.
Human Activity Recognitio, Cascade Learning;, Transfer Learning;, Deep learning
35-44
ACM
Du, Xin
9629013b-b962-4a81-bf18-7797d581fdd8
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Du, Xin
9629013b-b962-4a81-bf18-7797d581fdd8
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Du, Xin, Farrahi, Katayoun and Niranjan, Mahesan (2019) Transfer learning across human activities using a cascade neural network architecture. In ISWC '19 Proceedings of the 23rd International Symposium on Wearable Computers. ACM. pp. 35-44 . (doi:10.1145/3341163.3347730).

Record type: Conference or Workshop Item (Paper)

Abstract

Cascade Learning (CL) [20] is a new adaptive approach to train deep neural networks. It is particularly suited to transfer learning, as learning is achieved in a layerwise fashion, enabling the transfer of selected layers to optimize the quality of transferred features. In the domain of Human Activity Recognition (HAR), where the consideration of resource consumption is critical, CL is of particular interest as it has demonstrated the ability to achieve significant reductions in computational and memory costs with negligible performance loss. In this paper, we evaluate the use of CL and compare it to end to end (E2E) learning in various transfer learning experiments, all applied to HAR. We consider transfer learning across objectives, for example opening the door features transferred to opening the dishwasher. We additionally consider transfer across sensor locations on the body, as well as across datasets. Over all of our experiments, we find that CL achieves state of the art performance for transfer learning in comparison to previously published work, improving F1 scores by over 15%. In comparison to E2E learning, CL performs similarly considering F1 scores, with the additional advantage of requiring fewer parameters. Finally, the overall results considering HAR classification performance and memory requirements demonstrate that CL is a good approach for transfer learning.

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ISWC 2019 - Accepted Manuscript
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Accepted/In Press date: 16 July 2019
e-pub ahead of print date: 9 September 2019
Published date: 9 September 2019
Venue - Dates: 23rd International Symposium on Wearable Computers, United Kingdom, 2019-09-09 - 2019-09-13
Keywords: Human Activity Recognitio, Cascade Learning;, Transfer Learning;, Deep learning

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Local EPrints ID: 434822
URI: http://eprints.soton.ac.uk/id/eprint/434822
PURE UUID: 0a4b817b-9259-4196-9909-019e5167e8c7

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Date deposited: 11 Oct 2019 16:30
Last modified: 06 Oct 2020 17:48

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