Feature Recalibration in Deep Learning via Depthwise Squeeze and Refinement Operations
Feature recalibration is a very effective strategy of further improving performance in deep networks. The commonly used global pooling operation will lose the information of distinguishing features, which requires additional fully connected layers to adjust the relationship between feature maps. In...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9079495/ |