• Laser & Optoelectronics Progress
  • Vol. 58, Issue 22, 2211004 (2021)
Wenjun Chen, Chao Cong*, and Liwen Huang
Author Affiliations
  • College of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
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    DOI: 10.3788/LOP202158.2211004 Cite this Article Set citation alerts
    Wenjun Chen, Chao Cong, Liwen Huang. Convolutional Neural Network Image Feature Measurement Based on Information Entropy[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2211004 Copy Citation Text show less
    References

    [1] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 580-587(2014).

    [2] Feng X Y, Mei W, Hu D S. Aerial target detection based on improved Faster R-CNN[J]. Acta Optica Sinica, 38, 0615004(2018).

    [3] Ju M R, Luo H B, Wang Z B et al. Improved YOLO V3 algorithm and its application in small target detection[J]. Acta Optica Sinica, 39, 0715004(2019).

    [4] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 3431-3440(2015).

    [5] Wang J, Zhang X Y, Cai Y F et al. CT image segmentation method combining wavelet transform and RSF model[J]. Acta Optica Sinica, 40, 2110003(2020).

    [6] Yang G L, Lai Z D, Wang Y. Skin lesion image segmentation algorithm based on multi-scale DenseNet[J]. Laser & Optoelectronics Progress, 57, 181020(2020).

    [7] Cheng K Y, Wang N, Shi W X et al. Research advances in the interpretability of deep learning[J]. Journal of Computer Research and Development, 57, 1208-1217(2020).

    [8] Hua Y Y, Zhang D C, Ge S M. Research progress in the interpretability of deep learning models[J]. Journal of Cyber Security, 5, 1-12(2020).

    [9] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[M]. //Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8689, 818-833(2014).

    [10] Springenberg J T, Dosovitskiy A, Brox T et al. Striving for simplicity: the all convolutional net[EB/OL]. (2014-12-21)[2020-12-10]. https://arxiv.org/abs/1412.6806v3

    [11] Zhou B L, Khosla A, Lapedriza A et al. Learning deep features for discriminative localization[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 2921-2929(2016).

    [12] Selvaraju R R, Cogswell M, Das A et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision, 128, 336-359(2020).

    [13] Sturmfels P, Lundberg S, Lee S I. Visualizing the impact of feature attribution baselines[J]. Distill, 5, 22(2020).

    [14] Ribeiro M T, Singh S, Guestrin C. “Why should I trust You?”: explaining the predictions of any classifier[C]. //Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, California, USA, 1135-1144(2016).

    [15] Koh P W, Liang P. Understanding black-box predictions via influence functions[EB/OL]. (2017-03-14)[2020-12-10]. https://arxiv.org/abs/1703.04730

    [16] Wang H H, Wu X D, Huang Z Y et al. High-frequency component helps explain the generalization of convolutional neural networks[C]. //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA., 8681-8691(2020).

    [17] Liang R F, Li T L, Li L F et al. Knowledge consistency between neural networks and beyond[C]. //8th International Conference on Learning Representations (ICLR), April 26-30, 2020, Addis Ababa, Ethiopia(2020).

    [18] Ma H T, Zhang Y Q, Zhou F et al. Quantifying layerwise information discarding of neural networks[EB/OL]. (2019-06-10)[2020-12-10]. https://arxiv.org/abs/1906.04109

    [19] Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images[J]. Handbook of Systemic Autoimmune Diseases, 1, 1-60(2009).

    [20] Russakovsky O, Deng J, Su H et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 115, 211-252(2015).

    [21] Wang Z J, Turko R, Shaikh O et al. CNN explainer: learning convolutional neural networks with interactive visualization[J]. IEEE Transactions on Visualization and Computer Graphics, 27, 1396-1406(2021).

    [22] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

    [23] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

    [24] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-04)[2020-12-10]. https://arxiv.org/abs/1409.1556

    [25] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).

    [26] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7132-7141(2018).

    Wenjun Chen, Chao Cong, Liwen Huang. Convolutional Neural Network Image Feature Measurement Based on Information Entropy[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2211004
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