Advanced Deep Learning with Keras

Chapter 2
References
1. Kaiming He and others. Delving Deep into Rectifiers: Surpassing Human-Level
Performance on ImageNet Classification. Proceedings of the IEEE international
conference on computer vision, 2015 (https://www.cv-foundation.
org/openaccess/content_iccv_2015/papers/He_Delving_Deep_
into_ICCV_2015_paper.pdf?spm=5176.100239.blogcont55892.28.
pm8zm1&file=He_Delving_Deep_into_ICCV_2015_paper.pdf).
2. Kaiming He and others. Deep Residual Learning for Image Recognition.
Proceedings of the IEEE conference on computer vision and pattern
recognition, 2016a(http://openaccess.thecvf.com/content_cvpr_2016/
papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf).
3. Karen Simonyan and Andrew Zisserman. Very Deep Convolutional Networks
for Large-Scale Image Recognition. ICLR, 2015(https://arxiv.org/
pdf/1409.1556/).
4. Kaiming He and others. Identity Mappings in Deep Residual Networks.
European Conference on Computer Vision. Springer International
Publishing, 2016b(https://arxiv.org/pdf/1603.05027.pdf).
5. Gao Huang and others. Densely Connected Convolutional Networks.
Proceedings of the IEEE conference on computer vision and pattern
recognition, 2017(http://openaccess.thecvf.com/content_cvpr_2017/
papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.
pdf).
6. Saining Xie and others. Aggregated Residual Transformations for Deep Neural
Networks. Computer Vision and Pattern Recognition (CVPR), 2017 IEEE
Conference on. IEEE, 2017(http://openaccess.thecvf.com/content_
cvpr_2017/papers/Xie_Aggregated_Residual_Transformations_
CVPR_2017_paper.pdf).
7. Gustav Larsson, Michael Maire and Gregory Shakhnarovich. Fractalnet:
Ultra-Deep Neural Networks Without Residuals. arXiv preprint
arXiv:1605.07648, 2016 (https://arxiv.org/pdf/1605.07648.pdf).
[ 69 ]