A fast learning algorithm for deep belief nets, Neural Comput, vol.18, pp.1527-1554, 2006. ,
Reducing the dimensionality of data with neural networks, Science, pp.504-507, 2007. ,
Greedy layer-wise training of deep networks, Proc. NIPS, pp.153-160, 2007. ,
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Proc. ICML, pp.609-616, 2009. ,
Extracting and composing robust features with denoising autoencoders, Proc. ICML, pp.1096-1103, 2008. ,
, Deep learning, vol.521, pp.436-444, 2015.
Learning deep architectures for AI, Foundat. and Trends Mach. Learn, vol.2, pp.1-127, 2009. ,
, Deep Learning: Methods and Applications, Foundations and Trends® in Signal Processing, vol.7, pp.197-387, 2013.
Representation learning: A review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intell, vol.35, pp.1798-1828, 2013. ,
, Deep learning in neural networks: An overview, pp.85-117, 2015.
Backpropagation applied to handwritten zip code recognition, Neural Comput, vol.1, pp.541-551, 1989. ,
Gradient-based learning applied to document recognition, Proc. IEEE, vol.86, pp.2278-2324, 1998. ,
ImageNet large scale visual recognition challenge, Int. J. Comput. Vis, vol.115, pp.211-252, 2015. ,
ImageNet classification with deep convolutional neural network, Proc. NIPS, pp.1097-1105, 2012. ,
Going deeper with convolutions, Proc. IEEE Conf. CVPR, pp.1-9, 2015. ,
, Very deep convolutional networks for large-scale image recognition, 2014.
, Deep residual learning for image recognition, 2015.
, Labeled faces in the wild: A survey, 2015.
, DeepID3: Face recognition with very deep neural networks, 2015.
FaceNet: A unified embedding for face recognition and clustering, Computer Vision and Pattern Recognition(CVPR), (IEEE2015), pp.815-823 ,
Multi-column deep neural networks for image classification, Computer Vision and Pattern Recognition(CVPR), (IEEE2012), pp.3642-3649 ,
Constructing deep sparse coding network for image classification, Pattern Recognition, vol.64, pp.130-140, 2017. ,
Robust multi-atlas label propagation by deep sparse representation, Pattern Recognition, vol.63, pp.511-517, 2017. ,
Multi-scale volumes for deep object detection and localization, Pattern Recognition, vol.61, pp.557-572, 2017. ,
Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order, Pattern Recognition, pp.610-628, 2017. ,
Recurrent convolutional neural network for answer selection in community question answering, Neurocomputing, vol.274, pp.8-18, 2018. ,
Accurate seat belt detection in road surveillance images based on CNN and SVM, Neurocomputing, vol.274, pp.80-87, 2018. ,
A survey of deep neural network architectures and their applications, Neurocomputing, vol.234, pp.11-26, 2017. ,
Convolutional neural networks for hyperspectral image classification, Neurocomputing, vol.219, pp.88-98, 2017. ,
, Medical image retrieval using deep convolutional neural network, vol.266, pp.8-20, 2017.
Emotion-modulated attention improves expression recognition: A deep learning model, pp.104-114, 2017. ,
, A probabilistic theory of deep learning, 2015.
, An exact mapping between the variational renormalization group and deep learning, 2014.
, Statistical Physics: Statics, Dynamics and Renormalization, 2000.
, Deep learning and the information bottleneck principle, 2015.
The information sieve ,
Towards deep developmental learning, IEEE Trans. Auton. Mental Develop ,
URL : https://hal.archives-ouvertes.fr/hal-01331799
, A geometric view of optimal transportation and generative model, 2017.
How deep learning works-The geometry of deep learning, 2017. ,
Why does unsupervised deep learning work, A perspective from group theory, 2015. ,
, Provable approximation properties for deep neural networks, 2015.
DOI : 10.1016/j.acha.2016.04.003
URL : http://arxiv.org/pdf/1509.07385
On invariance and selectivity in representation learning, 2015. ,
DOI : 10.1093/imaiai/iaw009
URL : https://academic.oup.com/imaiai/article-pdf/5/2/134/6990886/iaw009.pdf
, Unsupervised learning of invariant representations in hierarchical architectures, 2013.
Group invariant scattering, Commun. Pure Appl. Math, vol.65, pp.1331-1398, 2012. ,
DOI : 10.1002/cpa.21413
URL : http://arxiv.org/pdf/1101.2286
Invariant scattering convolution networks, IEEE Trans. Pattern Anal. Mach. Intell, vol.35, pp.1872-1886, 2013. ,
DOI : 10.1109/tpami.2012.230
URL : http://arxiv.org/pdf/1203.1513
Deep convolutional neural networks based on semi-discrete frames, Proc. IEEE ISIT, pp.1212-1216, 2015. ,
DOI : 10.1109/isit.2015.7282648
URL : http://arxiv.org/pdf/1504.05487
, A theoretical argument for complex-valued convolutional networks, 2015.
Visualizing higher-layer features of a deep network, 2009. ,
An empirical evaluation of deep architectures on problems with many factors of variation, Proc. ICML, pp.473-480, 2007. ,
Measuring invariances in deep networks, Proc. NIPS, pp.646-654, 2009. ,
Intriguing properties of neural networks, Computer Science, pp.1-10, 2014. ,
Visualizing and understanding convolutional networks, ECCV, 2014. ,
DOI : 10.1007/978-3-319-10590-1_53
URL : http://cs.nyu.edu/%7Efergus/papers/zeilerECCV2014.pdf
Deep inside convolutional networks: Visualising image classification models and saliency maps, Proc. ICLR, 2014. ,
Rich feature hierarchies for accurate object detection and semantic segmentation, Computer Vision and Pattern Recognition (CVPR), IEEE Conference on, 2014. ,
DOI : 10.1109/cvpr.2014.81
URL : http://arxiv.org/pdf/1311.2524
Understanding deep image representations by inverting them, Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, (IEEE2015), pp.5188-5196 ,
DOI : 10.1109/cvpr.2015.7299155
URL : http://arxiv.org/pdf/1412.0035
PCANet: A simple deep learning baseline for image classification?, IEEE Trans. Image Process, vol.24, pp.5017-5032, 2015. ,
DOI : 10.1109/tip.2015.2475625
URL : http://arxiv.org/pdf/1404.3606
A deep graph embedding network model for face recognition, Proc. IEEE ICSP, pp.1268-1271, 2014. ,
DOI : 10.1109/icosp.2014.7015203
URL : http://arxiv.org/pdf/1409.7313.pdf
Unsupervised feature learning with C-SVDDNet, Pattern Recognition, vol.60, pp.473-485, 2016. ,
DOI : 10.1016/j.patcog.2016.06.001
URL : http://arxiv.org/pdf/1412.7259
DLANet: A manifold-learning-based discriminative feature learning network for scene classification, Neurocomputing, vol.157, pp.11-21, 2015. ,
DOI : 10.1016/j.neucom.2015.01.043
DCTNet: A simple learning-free approach for face recognition, Proceedings of APSIPA Annual Summit and Conference, pp.761-768, 2015. ,
DOI : 10.1109/apsipa.2015.7415375
URL : http://arxiv.org/pdf/1507.02049
, , 2015.
Multi-level modified finite radon transform network for image upsampling, IEEE Trans. Circuits Syst. Video Technol, vol.26, pp.2189-2199, 2015. ,
Learning stacked image descriptor for face recognition, IEEE Trans. Circuits Syst. Video Technol, vol.26, pp.1685-1696, 2016. ,
Feature learning based on SAE-PCA network for human gesture recognition in RGBD images, Neurocomputing, pp.565-573, 2015. ,
Color image classification via quaternion principal component analysis network, Neurocomputing, vol.216, pp.416-428, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01413815
Multilinear principal component analysis network for tensor object classification, IEEE Access, vol.5, pp.3322-3331, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01618929
The AR face database, CVC Technical Report, 1998. ,
The CMU pose, illumination, and expression (PIE) database, Proc. International Conference on Automatic Face and Gesture Recognition, 2002. ,
Learning multiple layers of features from tiny images, pp.1-58, 2009. ,
LIBLINEAR: A library for large linear classification, The Journal of Machine Learning Research, vol.9, pp.1871-1874, 2008. ,
An introduction to kernel and nearest-neighbor nonparametric regression, The American Statistician, vol.46, pp.175-185, 1992. ,
Orthogonal laplacianfaces for face recognition, IEEE Trans. Image Process, vol.15, pp.3608-3614, 2006. ,
, He is now working in the LIST as a lecturer. His research interest mainly includes deep learning, 2012.
, Shijie Qiu received the B.S. degree in Mathematical Science from Soochow University in 2015. Now she is currently pursuing the M.S. degree in Computer Science, Southeast University. Her research interests lie in deep learning and pattern recognition
, He is currently an Assistant Professor with the College of, respectively, and the Ph.D. degree in imaging and diagnostic radiology from the Chinese University of, 2008.
, Since 2013, she has been a Faculty Member with the Department of, 2013.
, He is currently an associate professor of the, His rece nt work concentrates on medical image reconstruction and analysis, 2001.
, he worked as an assistant professor at the School of Automation, Southeast University. Now he is an associate professor in the School of Automation, 2002.
, He is a Professor and the Head of the INSERM Research Laboratory LTSI. His is also Co-Director of the French-Chinese Laboratory CRIBs "Centre de Recherche en Information Biomédicale Sino-Français, His main research efforts are focused on nonstationary signal processing with particular emphasis on wavelet transforms and time-frequency representations for detection, classification, and interpretation of biosignals
, He is a professor of the LIST Laboratory and the Codirector of the CRIBs. His recent work concentrates on the image analysis, pattern recognition and fast algorithms of digital signal processing, Applied Mathematics from Wuhan University, China, in 1987, and a Ph.D. degree in Numerical Analysis from the University of Rennes 1, 1992.