SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). The SqueezeNet architecture is available for download here: https://github.com/DeepScale/SqueezeNet

References in zbMATH (referenced in 20 articles )

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  1. Baykal, Cenk; Liebenwein, Lucas; Gilitschenski, Igor; Feldman, Dan; Rus, Daniela: Sensitivity-informed provable pruning of neural networks (2022)
  2. Bernardo, Lucas Salvador; Damaševičius, Robertas; de Albuquerque, Victor Hugo C.; Maskeliūnas, Rytis: A hybrid two-stage squeezenet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns (2021)
  3. Fan, Jianqing; Ma, Cong; Zhong, Yiqiao: A selective overview of deep learning (2021)
  4. Huang, Junhao; Sun, Weize; Huang, Lei: Joint structure and parameter optimization of multiobjective sparse neural network (2021)
  5. Liu, Chunlei; Ding, Wenrui; Hu, Yuan; Zhang, Baochang; Liu, Jianzhuang; Guo, Guodong; Doermann, David: Rectified binary convolutional networks with generative adversarial learning (2021)
  6. Özcan, Hakan; Emiroğlu, Bülent Gürsel; Sabuncuoğlu, Hakan; Özdoğan, Selçuk; Soyer, Ahmet; Saygı, Tahsin: A comparative study for glioma classification using deep convolutional neural networks (2021)
  7. 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)
  8. Zhang, Xu; Huang, Wei; Gao, Jing; Wang, Dapeng; Bai, Changchuan; Chen, Zhikui: Deep sparse transfer learning for remote smart tongue diagnosis (2021)
  9. Bang, Duhyeon; Kang, Seoungyoon; Shim, Hyunjung: Discriminator feature-based inference by recycling the discriminator of GANs (2020)
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  11. Ju, Caleb; Solomonik, Edgar: Derivation and analysis of fast bilinear algorithms for convolution (2020)
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  14. Zheng, Qinghe; Tian, Xinyu; Yang, Mingqiang; Wu, Yulin; Su, Huake: PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning (2020)
  15. Dreossi, Tommaso; Donzé, Alexandre; Seshia, Sanjit A.: Compositional falsification of cyber-physical systems with machine learning components (2019)
  16. Kaiyang Zhou, Tao Xiang: Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch (2019) arXiv
  17. Tyukin, Ivan Yu.; Gorban, Alexander N.; Green, Stephen; Prokhorov, Danil: Fast construction of correcting ensembles for legacy artificial intelligence systems: algorithms and a case study (2019)
  18. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017) arXiv
  19. Rawat, Waseem; Wang, Zenghui: Deep convolutional neural networks for image classification: a comprehensive review (2017)
  20. Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices (2017) arXiv