Marginal Deep Architecture: Stacking Feature Learning Modules to Build Deep Learning Models
Recently, many deep models have been proposed in different fields, such as image classification, object detection, and speech recognition. However, most of these architectures require a large amount of training data and employ random initialization. In this paper, we propose to stack feature learnin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8657740/ |