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All issues Volume 28 / No 1 (February 2023) Wuhan Univ. J. Nat. Sci., 28 1 (2023) 35-44 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 1, February 2023
Page(s) 35 - 44
DOI https://doi.org/10.1051/wujns/2023281035
Published online 17 March 2023
  1. He Y, Zhao N, Yin H X. Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2018, 67(1): 44-55. [CrossRef] [Google Scholar]
  2. Zhao D B, Chen Y R, Lv L. Deep reinforcement learning with visual attention for vehicle classification[J]. IEEE Transactions on Cognitive and Developmental Systems, 2017, 9(4): 356-367. [CrossRef] [Google Scholar]
  3. Wang X, Yang W, Weinreb J, et al. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning[J]. Scientific Reports, 2017, 7(1): 15415. [Google Scholar]
  4. Xiong H Y, Alipanahi B, Lee L J, et al. The human splicing code reveals new insights into the genetic determinants of disease[J]. Science, 2015, 347(6218): 1254806. [CrossRef] [PubMed] [Google Scholar]
  5. Ching T, Himmelstein D S, Beaulieu-Jones B K, et al. Opportunities and obstacles for deep learning in biology and medicine[J]. Journal of the Royal Society Interface, 2018, 15(141): 20170387. [Google Scholar]
  6. Branson K. A deep (learning) dive into a cell[J]. Nature Methods, 2018, 15(4): 253-254. [CrossRef] [PubMed] [Google Scholar]
  7. Deng Y, Bao F, Kong Y Y, et al. Deep direct reinforcement learning for financial signal representation and trading[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(3): 653-664. [CrossRef] [PubMed] [Google Scholar]
  8. Szegedy C, Zaremba W, Sutskever I, et al. Intriguing properties of neural networks[EB/OL]. [2021-12-06].http://www.arXiv:1312.6199. [Google Scholar]
  9. Kurakin A, Goodfellow I J, Bengio S. Adversarial examples in the physical world[EB/OL]. [2022-02-15]. http://www.arXiv:1607.02533. [Google Scholar]
  10. Carlini N, Wagner D. Towards evaluating the robustness of neural networks[C]//2017 IEEE Symposium on Security and Privacy (SP). Washingtong D C: IEEE, 2017: 39-57. [Google Scholar]
  11. Xie C H, Zhang Z S, Zhou Y Y, et al. Improving transferability of adversarial examples with input diversity[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washingtong D C: IEEE, 2019: 2725-2734. [Google Scholar]
  12. Wu W B, Su Y X, Chen X X, et al. Boosting the transferability of adversarial samples via attention[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washingtong D C: IEEE, 2020: 1158-1167. [Google Scholar]
  13. Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization[C]//Proceedings of the IEEE International Conference on Computer Vision. Washingtong D C: IEEE, 2017: 618-626. [Google Scholar]
  14. Guo C, Gardner J R, You Y R, et al. Simple black-box adversarial attacks[EB/OL]. [2019-05-17]. https://doi.org/10.48550/arXiv.1905.07121. [Google Scholar]
  15. Dong Y P, Pang T Y, Su H, et al. Evading defenses to transferable adversarial examples by translation-invariant attacks[C]// The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Washingtong D C: IEEE, 2019:4307-4316. [Google Scholar]
  16. Wu W B, Su Y X, Lyu M R, et al. Improving the transferability of adversarial samples with adversarial transformations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washingtong D C: IEEE, 2021: 9020-9029. [Google Scholar]
  17. Papernot N, McDaniel P, Jha S, et al. The limitations of deep learning in adversarial settings[C]//2016 IEEE European Symposium on Security and Privacy (EuroS&P). Washington D C: IEEE, 2016: 372-387. [Google Scholar]
  18. Zhou W, Hou X, Chen Y, et al. Transferable adversarial perturbations[C]//Proceedings of the European Conference on Computer Vision (ECCV). Washingtong D C: IEEE, 2018: 452-467. [Google Scholar]
  19. Krizhevsky A. Learning Multiple Layers of Features from Tiny Images[D]. Tront: University of Tront, 2009. [Google Scholar]
  20. LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. [CrossRef] [Google Scholar]
  21. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2022-09-15]. http://www.arXiv:1409.1556. [Google Scholar]
  22. He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washingtong D C: IEEE, 2016: 770-778. [Google Scholar]
  23. Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washington D C: IEEE, 2016: 2818-2826. [Google Scholar]
  24. Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washingtong D C: IEEE, 2018: 7132-7141. [Google Scholar]
  25. Qin Z Q, Zhang P Y, Wu F, et al. FCAnet: Frequency channel attention networks[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Washingtong D C: IEEE, 2021: 763-772. [Google Scholar]
  26. Beyer L, Zhai X, Kolesnikov A. Better plain ViT baselines for ImageNet-1k[EB/OL]. [2021-12-05]. http://www.arXiv:2205.01580, 2022. [Google Scholar]

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