Bibiliography

[BRMW15]Anoop Korattikara Balan, Vivek Rathod, Kevin P Murphy, and Max Welling. Bayesian dark knowledge. In Advances in Neural Information Processing Systems, 3438–3446. 2015.
[BSF94]Yoshua Bengio, Patrice Simard, and Paolo Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2):157–166, 1994.
[DT05]Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, 886–893. IEEE, 2005.
[DDS+09]Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: a large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, 248–255. IEEE, 2009.
[DWSP12]Piotr Dollar, Christian Wojek, Bernt Schiele, and Pietro Perona. Pedestrian detection: an evaluation of the state of the art. IEEE transactions on pattern analysis and machine intelligence, 34(4):743–761, 2012.
[DJV+14]Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. Decaf: a deep convolutional activation feature for generic visual recognition. In International conference on machine learning, 647–655. 2014.
[FFFP06]Li Fei-Fei, Rob Fergus, and Pietro Perona. One-shot learning of object categories. IEEE transactions on pattern analysis and machine intelligence, 28(4):594–611, 2006.
[HZRS16]Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778. 2016.
[HVD15]Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.
[KS04]Yan Ke and Rahul Sukthankar. Pca-sift: a more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, II–II. IEEE, 2004.
[KHD11]Aniruddha Kembhavi, David Harwood, and Larry S Davis. Vehicle detection using partial least squares. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(6):1250–1265, 2011.
[KSH12]Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097–1105. 2012.
[LBD+90]Yann LeCun, Bernhard E Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne E Hubbard, and Lawrence D Jackel. Handwritten digit recognition with a back-propagation network. In Advances in neural information processing systems, 396–404. 1990.
[LBBH98]Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
[LMB+14]Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: common objects in context. In European conference on computer vision, 740–755. Springer, 2014.
[LSD15]Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431–3440. 2015.
[Low99]David G Lowe. Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, volume 2, 1150–1157. Ieee, 1999.
[LMT+07]Jun Luo, Yong Ma, Erina Takikawa, Shihong Lao, Masato Kawade, and Bao-Liang Lu. Person-specific sift features for face recognition. In Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, volume 2, II–593. IEEE, 2007.
[LS08]Siwei Lyu and Eero P Simoncelli. Nonlinear image representation using divisive normalization. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, 1–8. IEEE, 2008.
[OL13]Omar Oreifej and Zicheng Liu. Hon4d: histogram of oriented 4d normals for activity recognition from depth sequences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 716–723. 2013.
[RBK+14]Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. Fitnets: hints for thin deep nets. arXiv preprint arXiv:1412.6550, 2014.
[RHW85]David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning internal representations by error propagation. Technical Report, California Univ San Diego La Jolla Inst for Cognitive Science, 1985.
[SEZ+13]Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, and Yann LeCun. Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013.
[SZ14]Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[SHK+14]Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of machine learning research, 15(1):1929–1958, 2014.
[SGS15]Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. Highway networks. arXiv preprint arXiv:1505.00387, 2015.
[SAN16]Russell Stewart, Mykhaylo Andriluka, and Andrew Y. Ng. End-to-end people detection in crowded scenes. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2016.
[SMYC10]Ju Sun, Yadong Mu, Shuicheng Yan, and Loong-Fah Cheong. Activity recognition using dense long-duration trajectories. In Multimedia and Expo (ICME), 2010 IEEE International Conference on, 322–327. IEEE, 2010.
[SBM06]Zehang Sun, George Bebis, and Ronald Miller. On-road vehicle detection: a review. IEEE transactions on pattern analysis and machine intelligence, 28(5):694–711, 2006.
[SIVA17]Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, 4278–4284. 2017.
[SLJ+15]Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1–9. 2015.
[SVI+16]Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818–2826. 2016.
[vGSJC15]Bram van Ginneken, Arnaud AA Setio, Colin Jacobs, and Francesco Ciompi. Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 286–289. IEEE, 2015.
[VCLL12]Ragav Venkatesan, Parag Chandakkar, Baoxin Li, and Helen K Li. Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 1462–1465. IEEE, 2012.
[VL16]Ragav Venkatesan and Baoxin Li. Diving deeper into mentee networks. 2016.
[VLBM08]Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, 1096–1103. ACM, 2008.
[WOC+07]Jianxin Wu, Adebola Osuntogun, Tanzeem Choudhury, Matthai Philipose, and James M Rehg. A scalable approach to activity recognition based on object use. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 1–8. IEEE, 2007.
[ZvdM13]Lu Zhang and Laurens van der Maaten. Structure preserving object tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1838–1845. 2013.
[ZYS09]Huiyu Zhou, Yuan Yuan, and Chunmei Shi. Object tracking using sift features and mean shift. Computer vision and image understanding, 113(3):345–352, 2009.
[ZYCA06]Qiang Zhu, Mei-Chen Yeh, Kwang-Ting Cheng, and Shai Avidan. Fast human detection using a cascade of histograms of oriented gradients. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 2, 1491–1498. IEEE, 2006.