Accuracy Booster: Performance Boosting using Feature Map Re-calibration

Understand

Convolution Neural Networks (CNN) have been extremely successful in solving intensive computer vision tasks.

• The convolutional filters used in CNNs have played a major role in this success, by extracting useful features from the inputs.
• Recently researchers have tried to boost the performance of CNNs by re-calibrating the feature maps produced by these filters, e.g., Squeeze-and-Excitation Networks (SENets).
• These approaches have achieved better performance by Exciting up the important channels or feature maps while diminishing the rest.