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We use the scattering network as a generic and fixed ini-tialization of the first layers of a supervised hybrid deep network.
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Realistic modeling of simple and complex cell tuning in the hmax model, and implications for invariant object recognition in cortex
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Histograms of oriented gradients for human detection
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Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
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Convolutional networks and applications in vision
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Ask the locals: multi-way local pooling for image recognition
Y.-L. Boureau, N. Le Roux, F. Bach, J. Ponce, and Y. LeCun · 2011
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Selecting receptive fields in deep networks
A. Coates and A. Y. Ng · 2011
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Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng · 2011
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High-dimensional signature compression for large-scale image classification
J. Sánchez and F. Perronnin · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Group invariant scattering
S. Mallat · 2012
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Generalized analytic signals in image processing: comparison, theory and applications
S. Bernstein, J.-L. Bouchot, M. Reinhardt, and B. Heise · 2013
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Multipath sparse coding using hierarchical matching pursuit
L. Bo, X. Ren, and D. Fox · 2013
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Unsupervised feature learning for rgb-d based object recognition
L. Bo, X. Ren, and D. Fox · 2013
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Audio texture synthesis with scattering moments
J. Bruna and S. Mallat · 2013
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Invariant scattering convolution networks
J. Bruna and S. Mallat · 2013
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Learning stable group invariant representations with convolutional networks
J. Bruna, A. Szlam, and Y. LeCun · 2013
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Image classification with the fisher vector: Theory and practice
J. Sánchez, F. Perronnin, T. Mensink, and J. Verbeek · 2013
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Rotation, scaling and deformation invariant scattering for texture discrimination
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Deep roto-translation scattering for object classification
E. Oyallon and S. Mallat · 2015
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Fisher vectors meet neural networks: A hybrid classification architecture
F. Perronnin and D. Larlus · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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L. Sifre and S. Mallat · 2013
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Multi-task bayesian optimization
K. Swersky, J. Snoek, and R. P. Adams · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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These de doctorat de l’Ecole normale supérieure
I. Waldspurger · 2015
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Inverse problems with invariant multiscale statistics
I. Dokmanić, J. Bruna, S. Mallat, and M. de Hoop · 2016
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Deep unsupervised learning through spatial contrasting
E. Hoffer, I. Hubara, and N. Ailon · 2016
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What makes imagenet good for transfer learning?
M. Huh, P. Agrawal, and A. A. Efros · 2016
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Understanding deep convolutional networks
S. Mallat · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Stacked what-where auto-encoders
J. Zhao, M. Mathieu, R. Goroshin, and Y. LeCun · 2016
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Multiscale hierarchical convolutional networks
J.-H. Jacobsen, E. Oyallon, S. Mallat, and A. W. Smeulders · 2017
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Building a regular decision boundary with deep networks
E. Oyallon · 2017
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