ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves  13x actual speedup over AlexNet while maintaining comparable accuracy.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Liu, Chunlei; Ding, Wenrui; Hu, Yuan; Zhang, Baochang; Liu, Jianzhuang; Guo, Guodong; Doermann, David: Rectified binary convolutional networks with generative adversarial learning (2021)
  2. Qingzhong Wang, Pengfei Zhang, Haoyi Xiong, Jian Zhao: Face.evoLVe: A High-Performance Face Recognition Library (2021) arXiv
  3. Feng, Junxi; Teng, Qizhi; Li, Bing; He, Xiaohai; Chen, Honggang; Li, Yang: An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning (2020)
  4. Gao, Jing; Li, Peng; Chen, Zhikui; Zhang, Jianing: A survey on deep learning for multimodal data fusion (2020)
  5. Liu, Li; Ouyang, Wanli; Wang, Xiaogang; Fieguth, Paul; Chen, Jie; Liu, Xinwang; Pietikäinen, Matti: Deep learning for generic object detection: a survey (2020)
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  7. Sharma, Vipul; Mir, Roohie Naaz: A comprehensive and systematic look up into deep learning based object detection techniques: a review (2020)
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  9. Wang, Yan; Zhang, Hao; Xu, Lingwei; Cao, Conghui; Gulliver, T. Aaron: Adoption of hybrid time series neural network in the underwater acoustic signal modulation identification (2020)
  10. Zheng, Qinghe; Tian, Xinyu; Yang, Mingqiang; Wu, Yulin; Su, Huake: PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning (2020)
  11. Tripp, Bryan: Approximating the architecture of visual cortex in a convolutional network (2019)
  12. Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices (2017) arXiv