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Normalized Attention Inter-Channel Pooling (NAIP) for Deep Convolutional Neural Network Regularization

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Abstract

Deep learning methods have recently attracted attention in solving various tasks through their learning and determining capability for powerful features. It has been known that the absence of a pooling layer in the deep learning architectures made the network performance deteriorate significantly. Existing pooling methods assume that each value in the small region of the local feature map contributes equally to generate the pooled feature map. However, some values in the pooling region may contribute partially for generating the map. In addition, existing methods usually focused on the spatial dimension and ignored the inter-channel relationship during the pooling process. Besides, most of the pooling methods utilize a static pooling strategy which is designed based on the experts’ knowledge rather than learnt from the data. This study proposes a pooling method that is able to learn from the training data by utilizing a learnable attention mechanism, called Normalized Attention Inter-Channel Pooling (NAIP). The mechanism of the pooling process mainly focuses on the most important feature region for generating the pooled feature map. The proposed method is compared with the state-of-the-art works which use graph convolutional neural network and convolutional neural network architectures for skeleton-based human action recognition and image classification tasks. The experiment demonstrates that the NAIP approach outperforms the existing methods under the same circumstances.

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Acknowledgements

This research was supported by Hankuk University of Foreign Studies Research Fund and also supported by the Ministry of Science and ICT of the Republic of Korea and the National Research Foundation of Korea (NRF-2021R1F1A1047577).

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Authors and Affiliations

  1. Department of Statistics and Data Science, Yonsei University, Seoul, 120-749, Republic of Korea

    Feri Setiawan

  2. Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, Yongin-si, 17035, Republic of Korea

    Bernardo Nugroho Yahya & Seok-Lyong Lee

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  1. Feri Setiawan
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Correspondence to Bernardo Nugroho Yahya or Seok-Lyong Lee.

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Setiawan, F., Yahya, B.N. & Lee, SL. Normalized Attention Inter-Channel Pooling (NAIP) for Deep Convolutional Neural Network Regularization. Neural Process Lett 55, 9315–9333 (2023). https://doi.org/10.1007/s11063-023-11203-6

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