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About A New Channel Boosted Convolutional Neural Network Using Transfer Learning
1. The document proposes a novel Channel Boosted Convolutional Neural Network (CB-CNN) architecture that uses transfer learning to boost the input channels and improve the network's representational capacity. 2. The CB-CNN generates additional input channels from pretrained deep neural networks and generative models through transfer learning. It then exploits the original and boosted channels throughout the network to learn discriminative patterns. 3. The CB-CNN is evaluated on a telecom churn prediction task, which is challenging due to class imbalance, high dimensionality, and limited data availability. Preliminary results show the CB-CNN approach can better discern differences between churners and non-churners compared to other methods.
Detailed Information
| Author: | ['Mistlemagic'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 24 |
| Language: | English |
| Format: | |
| Price: | FREE |
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