MobileNets

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.


References in zbMATH (referenced in 22 articles , 1 standard article )

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  1. Aji, Sani; Kumam, Poom; Siricharoen, Punnarai; Bukar, Ali Maina; Adamu, Mohammed Sani: Deep transfer learning for automated artillery crater classification (2021)
  2. Efimov, Yu. S.; Leonov, V. Yu.; Odinokikh, G. A.; Solomatin, I. A.: Finding the iris using convolutional neural networks (2021)
  3. Gusak, J.; Daulbaev, T.; Ponomarev, E.; Cichocki, A.; Oseledets, I.: Reduced-order modeling of deep neural networks (2021)
  4. Hao, Jie; Zhu, William: Architecture self-attention mechanism: nonlinear optimization for neural architecture search (2021)
  5. Huang, Junhao; Sun, Weize; Huang, Lei: Joint structure and parameter optimization of multiobjective sparse neural network (2021)
  6. Khamparia, Aditya; Bharati, Subrato; Podder, Prajoy; Gupta, Deepak; Khanna, Ashish; Phung, Thai Kim; Thanh, Dang N. H.: Diagnosis of breast cancer based on modern mammography using hybrid transfer learning (2021)
  7. Liu, Chunlei; Ding, Wenrui; Hu, Yuan; Zhang, Baochang; Liu, Jianzhuang; Guo, Guodong; Doermann, David: Rectified binary convolutional networks with generative adversarial learning (2021)
  8. Peng, Jianzhong; Zhu, Wei; Liang, Qiaokang; Li, Zhengwei; Lu, Maoying; Sun, Wei; Wang, Yaonan: Defect detection in code characters with complex backgrounds based on BBE (2021)
  9. Tang, Meng; Liu, Yimin; Durlofsky, Louis J.: Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow (2021)
  10. Wang, Ruhua; An, Senjian; Liu, Wanquan; Li, Ling: Fixed-point algorithms for inverse of residual rectifier neural networks (2021)
  11. Xie, Yunxin; Zhu, Chenyang; Hu, Runshan; Zhu, Zhengwei: A coarse-to-fine approach for intelligent logging lithology identification with extremely randomized trees (2021)
  12. Yang, Hongfei; Ding, Xiaofeng; Chan, Raymond; Hu, Hui; Peng, Yaxin; Zeng, Tieyong: A new initialization method based on normed statistical spaces in deep networks (2021)
  13. Zhang, Xu; Huang, Wei; Gao, Jing; Wang, Dapeng; Bai, Changchuan; Chen, Zhikui: Deep sparse transfer learning for remote smart tongue diagnosis (2021)
  14. 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)
  15. Liu, Li; Ouyang, Wanli; Wang, Xiaogang; Fieguth, Paul; Chen, Jie; Liu, Xinwang; Pietikäinen, Matti: Deep learning for generic object detection: a survey (2020)
  16. Samsonov, N. A.; Gneushev, A. N.; Matveev, I. A.: Training a classifier by descriptors in the space of the Radon transform (2020)
  17. Sharma, Vipul; Mir, Roohie Naaz: A comprehensive and systematic look up into deep learning based object detection techniques: a review (2020)
  18. Sun, Rémy; Lampert, Christoph H.: KS(conf): a light-weight test if a multiclass classifier operates outside of its specifications (2020)
  19. Zheng, Qinghe; Tian, Xinyu; Yang, Mingqiang; Wu, Yulin; Su, Huake: PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning (2020)
  20. Huan, Er-Yang; Wen, Gui-Hua: Multilevel and multiscale feature aggregation in deep networks for facial constitution classification (2019)

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