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About Continuous Learning of Human Activity Models Using Deep Nets
This paper proposes a framework for continuously learning human activity models from streaming videos using deep learning and active learning. The framework uses a sparse autoencoder to learn features in an unsupervised manner and active learning to reduce manual labeling as new unlabeled instances arrive. Experiments on activity datasets demonstrate the effectiveness of the proposed framework.
Detailed Information
| Author: | ['Antun Tun'] |
|---|---|
| Publication Year: | 2015 |
| Pages: | 16 |
| Language: | English |
| Format: | |
| Price: | FREE |
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