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Transformation-invariant Gabor convolutional networks

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Abstract

Although deep convolutional neural networks (DCNNs) have powerful capability of learning complex feature representations, they are limited by poor ability in handling large rotations and scale transformations. In this paper, we propose a novel alternative to conventional convolutional layer named Gabor convolutional layer (GCL) to enhance the robustness to transformations. The GCL is a simple but efficient combination of Gabor prior knowledge and parameters learning. A GCL is composed of three components: Gabor extraction module, weight-sharing convolution module, and transformation pooling module, respectively. DCNNs integrated with GCLs, referred to as transformation-invariant Gabor convolutional networks (TI-GCNs), can be easily built by replacing standard convolutional layers with designed GCLs. Our experimental results on various real-world recognition tasks indicate that encoding traditional hand-crafted Gabor filters with dominant orientation and scale information into DCNNs is of great importance for learning compact feature representations and reinforcing the resistance to scale changes and orientation variations. The source code can be found at https://github.com/GuichenLv.

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Acknowledgements

This work is supported by the Shenzhen Science and Technology Innovation Committee (STIC) under Grant JCYJ20180306174455080.

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Authors and Affiliations

  1. The School of Automation, Southeast University, Nanjing, China

    Lei Zhuang, Feipeng Da & Shaoyan Gai

  2. The Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China

    Lei Zhuang, Feipeng Da & Shaoyan Gai

  3. Shenzhen Research Institute, Southeast University, Shenzhen, China

    Feipeng Da

  4. The National Research Center of Overseas Sinology, Beijing Foreign Studies University, Beijing, China

    Mengxiang Li

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  1. Lei Zhuang
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  2. Feipeng Da
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  3. Shaoyan Gai
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  4. Mengxiang Li
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Correspondence to Feipeng Da.

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Zhuang, L., Da, F., Gai, S. et al. Transformation-invariant Gabor convolutional networks. SIViP 14, 1413–1420 (2020). https://doi.org/10.1007/s11760-020-01684-6

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  • DOI: https://doi.org/10.1007/s11760-020-01684-6

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