DropFilterR: A Novel Regularization Method for Learning Convolutional Neural Networks

  • Hengyue PanEmail author
  • Xin Niu
  • Rongchun Li
  • Siqi Shen
  • Yong Dou


The past few years have witnessed the fast development of regularization methods for deep learning models such as fully-connected deep neural networks (DNNs) and convolutional neural networks (CNNs). Part of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks. DropConnect select to drop connections between fully-connected layers. By randomly discard some features or connections, the above mentioned methods relieve the overfitting problem and improve the performance of neural networks. In this paper, we proposed a novel regularization methods, namely DropFilterR, for the learning of CNNs. The basic idea of DropFilterR is to relax the rule of weight-sharing in CNNs by randomly drop elements in convolution filters. Specifically, we drop different elements in convolution filters along with their moving on input feature maps. Moreover, we may apply random drop rate to further increase the randomness of the proposed method. Also, we find a suitable way to accelerate the computation for DropFilterR based on theoretical analysis. Experimental results on several widely-used image databases such as MNIST, CIFAR-10 and Pascal VOC 2012 show that using DropFilterR improves performance on image classification tasks.


CNNs Regularization methods DropFilterR 



Funding was provided by National Key Research and Development Program of China (Grant No. 2018YFB1003405).


  1. 1.
    Ba J, Frey B (2013) Adaptive dropout for training deep neural networks. In: Advances in neural information processing systems, pp 3084–3092Google Scholar
  2. 2.
    Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009, IEEE, pp 248–255Google Scholar
  3. 3.
    Devries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. CoRR arXiv:1708.04552
  4. 4.
    Erhan D, Bengio Y, Courville A, Manzagol PA, Vincent P, Bengio S (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11(3):625–660MathSciNetzbMATHGoogle Scholar
  5. 5.
    Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The Pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303–338CrossRefGoogle Scholar
  6. 6.
    Ghiasi G, Lin T, Le QV (2018) Dropblock: A regularization method for convolutional networks. CoRR arXiv:1810.12890
  7. 7.
    He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385
  8. 8.
    Hong C, Yu J, Zhang J, Jin X, Lee KH (2019) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Ind Inf 15(7):3952–3961CrossRefGoogle Scholar
  9. 9.
    Huang G LZSDWK Sun Y (2016) Deep networks with stochastic depth. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision—ECCV 2016, vol 9908. Springer, ChamGoogle Scholar
  10. 10.
    Iosifidis A, Tefas A, Pitas I (2015) Dropelm: fast neural network regularization with dropout and dropconnect. Neurocomputing 162:57–66CrossRefGoogle Scholar
  11. 11.
    Korchi AE, Ghanou Y (2018) Dropweak: a novel regularization method of neural networks. Proc Comput Sci 127:102–108CrossRefGoogle Scholar
  12. 12.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  13. 13.
    McClure P, Kriegeskorte N (2016) Robustly representing uncertainty in deep neural networks through sampling. arXiv preprint arXiv:1611.01639
  14. 14.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
  15. 15.
    Smirnov EA, Timoshenko DM, Andrianov SN (2014) Comparison of regularization methods for imagenet classification with deep convolutional neural networks. Aasri Proc 6(1):89–94CrossRefGoogle Scholar
  16. 16.
    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958MathSciNetzbMATHGoogle Scholar
  17. 17.
    Tian ZCJNQ (2018) Dropfilter: dropout for convolutions. arXiv preprint arXiv:1810.09849
  18. 18.
    Tompson J, Goroshin R, Jain A, LeCun Y, Bregler C (2015) Efficient object localization using convolutional networks. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 648–656Google Scholar
  19. 19.
    Vedaldi A, Lenc K (2014) Matconvnet—convolutional neural networks for matlab. CoRR arXiv:1412.4564
  20. 20.
    Wager S, Wang S, Liang PS (2013) Dropout training as adaptive regularization. In: Advances in neural information processing systems, pp 351–359Google Scholar
  21. 21.
    Wan L, Zeiler M, Zhang S, LeCun Y, Fergus R (2013) Regularization of neural networks using dropconnect. In: Proceedings of the 30th international conference on machine learning (ICML-13), pp 1058–1066Google Scholar
  22. 22.
    Yang N, Tang H, Sun H, Yang X (2018) Dropband: a simple and effective method for promoting the scene classification accuracy of convolutional neural networks for vhr remote sensing imagery. IEEE Geosci Remote Sens Lett PP(99):1–5Google Scholar
  23. 23.
    Yao Y, Rosasco L, Caponnetto A (2007) On early stopping in gradient descent learning. Constr Approx 26(2):289–315MathSciNetCrossRefGoogle Scholar
  24. 24.
    Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032MathSciNetCrossRefGoogle Scholar
  25. 25.
    Yu J, Yang X, Gao F, Tao D (2016) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47(12):4014–4024CrossRefGoogle Scholar
  26. 26.
    Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell.
  27. 27.
    Yu J, Zhu C, Zhang J, Huang Q, Tao D (2019) Spatial pyramid-enhanced netvlad with weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst.
  28. 28.
    Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27(5):2420–2432MathSciNetCrossRefGoogle Scholar
  29. 29.
    Zhong Z, Zheng L, Kang G, Li S, Yang Y (2017) Random erasing data augmentation. arXiv:1708.04896

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina

Personalised recommendations