Abstract
AbstractPooling operations, essential for neural networks, reduce feature map dimensions while preserving key features and enhancing spatial invariance. Traditional pooling methods often miss the feature maps' alternating currentcomponents. This study introduces a novel global pooling technique utilizing spectral self‐attention, leveraging the discrete cosine transform for spectral analysis and a self‐attention mechanism for assessing frequency component significance. This approach allows for efficient feature synthesis through weighted averaging, significantly boosting TOP‐1 accuracy with minimal parameter increase, outperforming existing models.
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