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On Intra-Class Variance for Deep Learning of Classifiers Cover

On Intra-Class Variance for Deep Learning of Classifiers

Open Access
|Aug 2019

References

  1. [1] Badrinarayanan V., Kendall A., and Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv:1511.00561 [cs], Nov. 2015. arXiv: 1511.00561.
  2. [2] Chen L.-C., Papandreou G., Kokkinos I., Murphy K., and Yuille A. L. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. arXiv:1606.00915 [cs], June 2016. arXiv: 1606.00915.
  3. [3] Fisher R. A. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2):179–188, Sept. 1936.10.1111/j.1469-1809.1936.tb02137.x
  4. [4] Hadsell R., Chopra S., and LeCun Y. Dimensionality reduction by learning an invariant mapping. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, CVPR ’06, pages 1735–1742, Washington, DC, USA, 2006. IEEE Computer Society.
  5. [5] He K., Gkioxari G., Dollár P., and Girshick R. Mask R-CNN. arXiv:1703.06870 [cs], Mar. 2017. arXiv: 1703.06870.10.1109/ICCV.2017.322
  6. [6] He K., Zhang X., Ren S., and Sun J. Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs], Dec. 2015. arXiv: 1512.03385.10.1109/CVPR.2016.90
  7. [7] Jourabloo A. and Liu X. Large-Pose Face Alignment via CNN-Based Dense 3d Model Fitting. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4188–4196, Las Vegas, NV, USA, June 2016. IEEE.10.1109/CVPR.2016.454
  8. [8] Kingma D. P. and Ba J. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
  9. [9] Kowalski M. and Naruniec J. (Warsaw University of Technology – personal communication).
  10. [10] Lecun Y. and Cortes C. The MNIST database of handwritten digits.
  11. [11] Ren S., He K., Girshick R., and Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137–1149, June 2017.10.1109/TPAMI.2016.257703127295650
  12. [12] Rosenblatt F. The perceptron–a perceiving and recognizing automaton. report 85-460-1. Technical report, Cornell Aeronautical Laboratory, 1957.
  13. [13] Rumelhart D. E., Hinton G. E., and Williams R. J. Learning representations by back-propagating errors. In Anderson J. A. and Rosenfeld E., editors, Neurocomputing: Foundations of Research, pages 696–699. MIT Press, Cambridge, MA, USA, 1988.10.7551/mitpress/4943.003.0042
  14. [14] Schmidhuber J. Deep learning in neural networks: An overview. CoRR, abs/1404.7828, 2014.
  15. [15] Schro F., Kalenichenko D., and Philbin J. FaceNet: A unified embedding for face recognition and clustering. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 815–823, Boston, MA, USA, June 2015. IEEE.10.1109/CVPR.2015.7298682
  16. [16] Simonyan K. and Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs], Sept. 2014. arXiv: 1409.1556.
  17. [17] Skarbek W. Symbolic tensor neural networks for digital media - from tensor processing via bnf graph rules to creams applications. Preprint stored in Cornell University Archive, abs/1809.06582, 2018.
  18. [18] Skarbek W., Kucharski K., and Bober M. Dual lda for face recognition. Fundam. Inform., 61:303–334, 07 2004.
  19. [19] Wen Y., Zhang K., Li Z., and Qiao Y. A Discriminative Feature Learning Approach for Deep Face Recognition. In Leibe B., Matas J., Sebe N., and Welling M., editors, Computer Vision – ECCV 2016, volume 9911, pages 499–515. Springer International Publishing, Cham, 2016.10.1007/978-3-319-46478-7_31
  20. [20] Werbos P. J. Applications of advances in nonlinear sensitivity analysis. In Drenick R. F. and Kozin F., editors, System Modeling and Optimization, pages 762–770, Berlin, Heidelberg, 1982. Springer Berlin Heidelberg.10.1007/BFb0006203
  21. [21] Xiao H., Rasul K., and Vollgraf R. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv:1708.07747 [cs, stat], Aug. 2017. arXiv: 1708.07747.
  22. [22] Zhang K., Zhang Z., Li Z., and Qiao Y. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks. IEEE Signal Processing Letters, 23(10):1499–1503, Oct. 2016.10.1109/LSP.2016.2603342
DOI: https://doi.org/10.2478/fcds-2019-0015 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Language: English
Page range: 285 - 301
Submitted on: Jan 29, 2019
Accepted on: Apr 23, 2019
Published on: Aug 28, 2019
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2019 Rafał Pilarczyk, Władysław Skarbek, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.