Cite

[1] Bowyer K. W., Chawla N. V., Hall L. O., and Kegelmeyer W. P. SMOTE: synthetic minority over-sampling technique. CoRR, abs/1106.1813, 2011.Search in Google Scholar

[2] Chollet F. Xception: Deep learning with depthwise separable convolutions. CoRR, abs/1610.02357, 2016.10.1109/CVPR.2017.195Search in Google Scholar

[3] Garland M., Jaworek-Korjakowska J., Libal U., Bogyo M., and M. S. An automatic analysis system for high-throughput clostridium di cile toxin activity screening. Applied Science, 8(1512), 2018.10.3390/app8091512Search in Google Scholar

[4] He K., Zhang X., Ren S., and Sun J. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015.Search in Google Scholar

[5] Huang G., Liu Z., van der Maaten L., and Weinberger K. Q. Densely connected convolutional networks, 2016.10.1109/CVPR.2017.243Search in Google Scholar

[6] Jaworek-Korjakowska J., Kleczek P., and Gorgon M. Melanoma thickness prediction based on convolutional neural network with VGG-19 model transfer learning. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019.10.1109/CVPRW.2019.00333Search in Google Scholar

[7] Krizhevsky A., Sutskever I., and Hinton G. E. Imagenet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS’12, pages 1097–1105, USA, 2012. Curran Associates Inc.Search in Google Scholar

[8] Lin M., Chen Q., and Yan S. Network in network. International Conference on Learning Representations, 2014.Search in Google Scholar

[9] Lin T.-Y., Maire M., Belongie S., Bourdev L., Girshick R., Hays J., Perona P., Ramanan D., Zitnick C. L., and Dollár P. Microsoft coco: Common objects in context, 2014.10.1007/978-3-319-10602-1_48Search in Google Scholar

[10] Medium.com. Review: AlexNet, Ca eNet — winner of ILSVRC 2012 (image classification). https://medium.com/coinmonks/paper-review-of-alexnetcaenet-winner-in-ilsvrc-2012-image-classification-b93598314160, 2018. [Online; accessed 20.06.2020].Search in Google Scholar

[11] Pan S. J. and Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359, Oct 2010.10.1109/TKDE.2009.191Search in Google Scholar

[12] Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A. C., and Fei-Fei L. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211–252, 2015.10.1007/s11263-015-0816-ySearch in Google Scholar

[13] Simonyan K. and Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv:1409.1556, 2014.Search in Google Scholar

[14] Szegedy C., Liu W., Jia Y., Sermanet P., Reed S. E., Anguelov D., Erhan D., Vanhoucke V., and Rabinovich A. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition, 2014.10.1109/CVPR.2015.7298594Search in Google Scholar

[15] Tan C., Sun F., Kong T., Zhang W., Yang C., and Liu C. A survey on deep transfer learning. CoRR, abs/1808.01974, 2018.Search in Google Scholar

[16] Torrey L. and Shavlik J. Transfer learning. Handbook of Research on Machine Learning Applications, 01 2009.10.4018/978-1-60566-766-9.ch011Search in Google Scholar

[17] Yosinski J., Clune J., Bengio Y., and Lipson H. How transferable are features in deep neural networks? In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, pages 3320–3328, Cambridge, MA, USA, 2014. MIT Press.Search in Google Scholar

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