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HDAM: Heuristic Difference Attention Module for Convolutional Neural Networks
Most attention mechanisms are bound to the convolutional layer and use local or global contextual information to recalibrate the input.
A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick
In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture.
ConAM: Confidence Attention Module for Convolutional Neural Networks
It uses contextual information to recalibrate the input to strengthen the propagation of informative features.
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