Fetching the paper…
Reading the bibliography…
We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D.G · 2004
Earlier work this paper cites.
Integral invariants for shape matching
Manay, Siddharth, Cremers, Daniel, Hong, Byung Woo, Yezzi, Anthony J., and Soatto, Stefano · 2006
Earlier work this paper cites.
A novel set of rotationally and translationally invariant features for images based on the non-commutative bispectrum
Kondor, R · 2007
Earlier work this paper cites.
An empirical evaluation of deep architectures on problems with many factors of variation
Larochelle, H., Erhan, D., Courville, A., Bergstra, J., and Bengio, Y · 2007
Earlier work this paper cites.
Group Integration Techniques in Pattern Analysis
Reisert, Marco · 2008
Earlier work this paper cites.
Transforming auto-encoders
Hinton, G. E., Krizhevsky, A., and Wang, S. D · 2011
Earlier work this paper cites.
Transformation equivariant Boltzmann machines
Kivinen, Jyri J. and Williams, Christopher K I · 2011
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
Earlier work this paper cites.
Learning rotation-aware features: From invariant priors to equivariant descriptors
Schmidt, U. and Roth, S · 2012
Earlier work this paper cites.
Learning Invariant Representations with Local Transformations
Sohn, K. and Lee, H · 2012
Earlier work this paper cites.
Invariant scattering convolution networks
Bruna, J. and Mallat, S · 2013
Earlier work this paper cites.
Maxout Networks
Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y · 2013
Earlier work this paper cites.
Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination
Sifre, Laurent and Mallat, Stephane · 2013
Earlier work this paper cites.
Spherical Tensor Algebra for Biomedical Image Analysis
Skibbe, H · 2013
Earlier work this paper cites.
Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., LeCun, Y., and Fergus, R · 2013
Cited alongside, same era.
Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?
Anselmi, F., Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., and Poggio, T · 2014
Cited alongside, same era.
Learning the Irreducible Representations of Commutative Lie Groups
Cohen, T. and Welling, M · 2014
Cited alongside, same era.
Deep Symmetry Networks
Gens, R. and Domingos, P · 2014
Cited alongside, same era.
Graham, B · 2014
Cited alongside, same era.
Network In Network
Lin, M., Chen, Q., and Yan, S · 2014
Spatial Transformer Networks
Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K · 2015
Later among the works it cites.
Adam: A Method for Stochastic Optimization
Kingma, D. and Ba, J · 2015
Later among the works it cites.
Fast Algorithms for Convolutional Neural Networks
Lavin, A. and Gray, S · 2015
Later among the works it cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Later among the works it cites.
Understanding image representations by measuring their equivariance and equivalence
Lenc, K. and Vedaldi, A · 2015
Later among the works it cites.
Deep Roto-Translation Scattering for Object Classification
Oyallon, E. and Mallat, S · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Fast Training of Convolutional Networks through FFTs
Mathieu, M., Henaff, M., and LeCun, Y · 2014
Cited alongside, same era.
Learning to See by Moving
Agrawal, P., Carreira, J., and Malik, J · 2015
Cited alongside, same era.
On Invariance and Selectivity in Representation Learning
Anselmi, F., Rosasco, L., and Poggio, T · 2015
Cited alongside, same era.
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D., Unterthiner, T., and Hochreiter, S · 2015
Cited alongside, same era.
Transformation Properties of Learned Visual Representations
Cohen, T. S. and Welling, M · 2015
Cited alongside, same era.
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Dieleman, S., Willett, K. W., and Dambre, J · 2015
Cited alongside, same era.
Striving for Simplicity: The All Convolutional Net
Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
Later among the works it cites.
Training Very Deep Networks
Srivastava, Rupesh Kumar, Greff, Klaus, and Schmidhuber, Jürgen · 2015
Later among the works it cites.
Fast convolutional nets with fbfft: A GPU performance evaluation
Vasilache, N., Johnson, J., Mathieu, M., Chintala, S., Piantino, S., and LeCun, Y · 2015
Later among the works it cites.
Discriminative template learning in group-convolutional networks for invariant speech representations
Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L., and Poggio, T · 2015
Later among the works it cites.
Exploiting Cyclic Symmetry in Convolutional Neural Networks
Dieleman, S., De Fauw, J., and Kavukcuoglu, K · 2016
Closest in time.
Identity Mappings in Deep Residual Networks
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
Closest in time.
Zagoruyko, S. and Komodakis, N · 2016
Closest in time.