AlexNet is a convolutional neural network that is 8 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 227-by-227. For more pretrained networks in MATLAB®, see Pretrained Deep Neural Networks.

References in zbMATH (referenced in 372 articles )

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  1. Adcock, Ben; Dexter, Nick: The gap between theory and practice in function approximation with deep neural networks (2021)
  2. Ao, Wenqi; Li, Wenbin; Qian, Jianliang: A data and knowledge driven approach for SPECT using convolutional neural networks and iterative algorithms (2021)
  3. Celledoni, Elena; Ehrhardt, Matthias J.; Etmann, Christian; Owren, Brynjulf; Schönlieb, Carola-Bibiane; Sherry, Ferdia: Equivariant neural networks for inverse problems (2021)
  4. Chen, Tianbo; Sun, Ying; Li, Ta-Hsin: A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network (2021)
  5. Chi, Heng; Zhang, Yuyu; Tang, Tsz Ling Elaine; Mirabella, Lucia; Dalloro, Livio; Song, Le; Paulino, Glaucio H.: Universal machine learning for topology optimization (2021)
  6. De Loera, Jesús A.; Haddock, Jamie; Ma, Anna; Needell, Deanna: Data-driven algorithm selection and tuning in optimization and signal processing (2021)
  7. Effland, Alexander; Kobler, Erich; Pock, Thomas; Rajković, Marko; Rumpf, Martin: Image morphing in deep feature spaces: theory and applications (2021)
  8. Fan, Jianqing; Ma, Cong; Zhong, Yiqiao: A selective overview of deep learning (2021)
  9. Fresca, Stefania; Dede’, Luca; Manzoni, Andrea: A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (2021)
  10. Gambella, Claudio; Ghaddar, Bissan; Naoum-Sawaya, Joe: Optimization problems for machine learning: a survey (2021)
  11. Gao, Yu; Zhang, Kai: Machine learning based data retrieval for inverse scattering problems with incomplete data (2021)
  12. Gordon, Andrew S. (ed.); Miller, Rob (ed.); Morgenstern, Leora (ed.); Turán, György (ed.): Preface (2021)
  13. Guo, Rui; Zhou, Yong; Zhao, Jiaqi; Yao, Rui; Liu, Bing; Zhang, Xunhui: Unsupervised spatial-awareness attention-based and multi-scale domain adaption network for point cloud classification (2021)
  14. Guo, Zhenfei; Bai, Ruixiang; Lei, Zhenkun; Jiang, Hao; Liu, Da; Zou, Jianchao; Yan, Cheng: CPINet: parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM (2021)
  15. Haghighat, Ehsan; Juanes, Ruben: SciANN: a keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (2021)
  16. Hao, Jie; Zhu, William: Architecture self-attention mechanism: nonlinear optimization for neural architecture search (2021)
  17. Harris, Ethan; Mihai, Daniela; Hare, Jonathon: How convolutional neural network architecture biases learned opponency and color tuning (2021)
  18. Huang, Junhao; Sun, Weize; Huang, Lei: Joint structure and parameter optimization of multiobjective sparse neural network (2021)
  19. Ivek, Tomislav; Vlah, Domagoj: BlackBox: generalizable reconstruction of extremal values from incomplete spatio-temporal data (2021)
  20. Jia, Fan; Liu, Jun; Tai, Xue-Cheng: A regularized convolutional neural network for semantic image segmentation (2021)

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