On the Traceability of Results from Deep Learning-Based Cloud Services

Bailer, Werner

doi:10.1007/978-3-319-73603-7_50

MultiMedia Modeling

2018

DOI: 10.1007/978-3-319-73603-7_50

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On the Traceability of Results from Deep Learning-Based Cloud Services

Werner Bailer

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2019

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Resource-Efficient Object Detection by Sharing Backbone CNNs

Bailer

Fassold

2019

2019 IEEE International Symposium on Multimedia (ISM)

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The detection of objects in image and video has made huge progress in recent years due to the use of deep convolutional neural networks (DNNs), with some network architectures becoming de-facto standards. This paper addresses the problem of sharing a backbone CNN for different tasks, for example, to enable detection of additional classes when an already trained network is available. When using multiple such neural networks, sharing a backbone can save inference time and memory consumption. We study sharing a common backbone between neural networks trained for different tasks (logoness and text block detection) based on Yolo v3. We provide results on the impact of different lengths of the shared backbone on performance and resource efficiency.

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Resource-Efficient Object Detection by Sharing Backbone CNNs

Bailer

Fassold

2019

2019 IEEE International Symposium on Multimedia (ISM)

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On the Traceability of Results from Deep Learning-Based Cloud Services

Cited by 1 publication

References 14 publications

Resource-Efficient Object Detection by Sharing Backbone CNNs

Resource-Efficient Object Detection by Sharing Backbone CNNs

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