Phân Tích Kim Loại Định Lượng Năng Suất Cao Đối Với Các Cấu Trúc Vi Khó Khăn Sử Dụng Học Sâu: Nghiên Cứu Ví Dụ Trong Thép Carbon Siêu Cao

Jégou S , Drozdzal M , Vazquez D , Romero A & Bengio Y (2016). The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation, CoRR. Available at http://arxiv.org/abs/1611.09326v2.

Bansal A , Chen X , Russell B , Gupta A & Ramanan D (2017). PixelNet: Representation of the pixels, by the pixels, and for the pixels, CoRR. Available at http://arxiv.org/abs/1702.06506v1.

Goodfellow, 2016, Deep Learning

10.1109/TPAMI.2016.2644615

10.1109/34.87344

10.1007/978-3-319-24574-4_28

10.1109/ICCV.2015.123

Chollet F (2015). Keras. Available at https://github.com/fchollet/keras.

Zener, 1948, Quoted by CS Smith, Trans AIME, 175, 15

Nair V & Hinton GE (2010). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML'10, 807–814, Omnipress. Available at http://dl.acm.org/citation.cfm?id=3104322.3104425.

10.1038/nature14539

10.1038/s41598-018-20037-5

10.1109/CVPR.2015.7298965

10.1016/B978-0-08-051581-6.50024-6

10.1016/j.actamat.2017.05.014

10.1007/s40192-017-0097-0

10.1016/j.commatsci.2015.08.011

Loshchilov I & Hutter F (2017). Fixing weight decay regularization in adam, CoRR. Available at http://arxiv.org/abs/1711.05101v2.

10.1088/0370-1301/64/9/303

Hariharan B , Arbelaez P , Girshick R & Malik J (2015). Hypercolumns for object segmentation and fine-grained localization. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

10.1007/978-0-387-21606-5

Hecht MD , Decost BL , Francis T , Holm EA , Picard YN & Webler BA (2017 a). Ultrahigh carbon steel micrographs. Available at https://hdl.handle.net/11256/940.

10.1007/s11661-017-4012-2

10.1016/j.matchar.2016.04.012

Kingma DP & Ba J (2014). Adam: A method for stochastic optimization, CoRR. Available at http://arxiv.org/abs/1412.6980v9.

Lin TY , Goyal P , Girshick R , He K & Dollár P (2017). Focal loss for dense object detection, CoRR. Available at http://arxiv.org/abs/1708.02002v2.

Lubbers N , Lookman T & Barros K (2016). Inferring low-dimensional microstructure representations using convolutional neural networks, CoRR. Available at http://arxiv.org/abs/1611.02764v1.

10.1080/01621459.1951.10500769

10.1109/TSMC.1979.4310076

Pedregosa, 2011, Scikit-learn: Machine learning in python, The Journal of Machine Learning Research, 12, 2825

10.1007/s11263-015-0816-y

Simonyan K & Zisserman A (2014). Very deep convolutional networks for large-scale image recognition, CoRR abs/1409.1556. Available at http://arxiv.org/abs/1409.1556.

Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J Mach Learn Res, 15, 1929

10.7717/peerj.453

10.1016/j.commatsci.2016.05.034

Ioffe, 2015, Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, 37, 448

Decost BL , Hecht MD , Sibley T , Francis T , Webler BA , Picard Y & Holm EA (2018). Ultrahigh carbon steel micrographs: Microconstituent annotations. Available at https://hdl.handle.net/11256/964.

Bansal A , Russell B & Gupta A (2016). Marr revisited: 2d–3d alignment via surface normal prediction. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5965–5974.

Kendall A , Badrinarayanan V & Cipolla R (2015). Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding, CoRR. Available at http://arxiv.org/abs/1511.02680v2.

10.1007/s11548-013-0840-8

Wang P , Chen P , Yuan Y , Liu D , Huang Z , Hou X & Cottrell G (2017). Understanding convolution for semantic segmentation, CoRR. Available at http://arxiv.org/abs/1702.08502v1.