OneNet: A Channel-Wise 1D Convolutional U-Net

• URL: http://arxiv.org/abs/2411.09838v1
• Date: Thu, 14 Nov 2024 23:11:45 GMT
• Title: OneNet: A Channel-Wise 1D Convolutional U-Net
• Authors: Sanghyun Byun, Kayvan Shah, Ayushi Gang, Christopher Apton, Jacob Song, Woo Seong Chung,
• Abstract summary: We present a 1D convolutional encoder that retains accuracy while enhancing its suitability for edge applications. OneNet captures spatial relationships without requiring 2D convolutions, reducing parameters by up to 47%. We benchmark our approach against U-Net variants across diverse mask-generation tasks, demonstrating that it preserves accuracy effectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many state-of-the-art computer vision architectures leverage U-Net for its adaptability and efficient feature extraction. However, the multi-resolution convolutional design often leads to significant computational demands, limiting deployment on edge devices. We present a streamlined alternative: a 1D convolutional encoder that retains accuracy while enhancing its suitability for edge applications. Our novel encoder architecture achieves semantic segmentation through channel-wise 1D convolutions combined with pixel-unshuffle operations. By incorporating PixelShuffle, known for improving accuracy in super-resolution tasks while reducing computational load, OneNet captures spatial relationships without requiring 2D convolutions, reducing parameters by up to 47%. Additionally, we explore a fully 1D encoder-decoder that achieves a 71% reduction in size, albeit with some accuracy loss. We benchmark our approach against U-Net variants across diverse mask-generation tasks, demonstrating that it preserves accuracy effectively. Although focused on image segmentation, this architecture is adaptable to other convolutional applications. Code for the project is available at https://github.com/shbyun080/OneNet .

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