Bi-branch deconvolution-based convolutional neural network for image classification


With the rise of deep neural network, convolutional neural networks show superior performances on many different computer vision recognition tasks. The convolution is used as one of the most efficient ways for extracting the details features of an image, while the deconvolution is mostly used for semantic segmentation and significance detection to obtain the contour information of the image and rarely used for image classification. In this paper, we propose a novel network named bi-branch deconvolution-based convolutional neural network (BB-deconvNet), which is constructed by mainly stacking a proposed simple module named Zoom. The Zoom module has two branches to extract multi-scale features from the same feature map. Especially, the deconvolution is borrowed to one of the branches, which can provide distinct features differently from regular convolution through the zoom of learned feature maps. To verify the effectiveness of the proposed network, we conduct several experiments on three object classification benchmarks (CIFAR-10, CIFAR-100, SVHN). The BB-deconvNet shows encouraging performances compared with other state-of-the-art deep CNNs.

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This work is supported by the Natural Science Foundation of China (Grant 61572214 and U1536203), Independent Innovation Research Fund Sponsored by Huazhong university of science and technology (Project No. 2016YXMS089).

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Correspondence to Jingjuan Guo or Tianjiang Wang.

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Guo, J., Yuan, C., Zhao, Z. et al. Bi-branch deconvolution-based convolutional neural network for image classification. Multimed Tools Appl 77, 30233–30250 (2018).

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  • Image classification
  • Bi-branch convolutional neural network
  • Deconvolution
  • Multi-scale