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arXiv · 2503.22841

GmNet: Revisiting Gating Mechanisms From A Frequency View

Yifan Wang·Xu Ma·Yitian Zhang·Zhongruo Wang·Sung-Cheol Kim·Vahid Mirjalili·Vidya Renganathan·Yun Fu
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Abstract

Gating mechanisms have emerged as an effective strategy integrated into model designs beyond recurrent neural networks for addressing long-range dependency problems. In a broad understanding, it provides adaptive control over the information flow while maintaining computational efficiency. However, there is a lack of theoretical analysis on how the gating mechanism works in neural networks. In this paper, inspired by the \textit{convolution theorem}, we systematically explore the effect of gating mechanisms on the training dynamics of neural networks from a frequency perspective. We investigate the interact between the element-wise product and activation functions in managing the responses to different frequency components. Leveraging these insights, we propose a Gating Mechanism Network (GmNet), a lightweight model designed to efficiently utilize the information of various frequency components. It minimizes the low-frequency bias present in existing lightweight models. GmNet achieves impressive performance in terms of both effectiveness and efficiency in the image classification task.

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Yifan Wang, Xu Ma, Yitian Zhang, Zhongruo Wang, Sung-Cheol Kim, Vahid Mirjalili, Vidya Renganathan, Yun Fu. 2026-02-26. GmNet: Revisiting Gating Mechanisms From A Frequency View. https://arxiv.org/abs/2503.22841

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