Deep Rotation Equivariant Network

Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11) (1998) 2278–2324

1998

Earlier work this paper cites.

B. Fasel, D. Gatica-Perez, Rotation-invariant neoperceptron, in: 18th International Conference on Pattern Recognition (ICPR’06), Vol. 3, IEEE, 2006, pp. 336–339

2006

Earlier work this paper cites.

H. Larochelle, D. Erhan, A. Courville, J. Bergstra, Y. Bengio, An empirical evaluation of deep architectures on problems with many factors of variation, in: Proceedings of the 24th international conference on Machine learning, ACM, 2007, pp. 473–480

2007

Earlier work this paper cites.

V. Volkov, J. W. Demmel, Benchmarking gpus to tune dense linear algebra, in: High Performance Computing, Networking, Storage and Analysis, 2008. SC 2008. International Conference for, IEEE, 2008, pp. 1–11

2008

Earlier work this paper cites.

K. Kavukcuoglu, R. Fergus, Y. LeCun, et al., Learning invariant features through topographic filter maps, in: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 2009, pp. 1605–1612

2009

Earlier work this paper cites.

M. Norouzi, M. Ranjbar, G. Mori, Stacks of convolutional restricted boltzmann machines for shift-invariant feature learning, in: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 2009, pp. 2735–2742

2009

Earlier work this paper cites.

H. Lee, R. Grosse, R. Ranganath, A. Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, in: Proceedings of the 26th annual international conference on machine learning, ACM, 2009, pp. 609–616

2009

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in neural information processing systems, 2012, pp. 1097–1105

2012

Earlier work this paper cites.

I. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, Y. Bengio, Maxout networks, in: Proceedings of The 30th International Conference on Machine Learning, 2013, pp. 1319–1327

2013

Cited alongside, same era.

R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580–587

2014

Cited alongside, same era.

R. Gens, P. M. Domingos, Deep symmetry networks, in: Advances in neural information processing systems, 2014, pp. 2537–2545

2014

Cited alongside, same era.

M. Lin, Q. Chen, S. Yan, Network in network, in: In Proc. ICLR, 2014

2014

Cited alongside, same era.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9

S. Dieleman, J. D. Fauw, K. Kavukcuoglu, Exploiting cyclic symmetry in convolutional neural networks, in: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, 2016, pp. 1889–1898

2016

Later among the works it cites.

K. Grzegorczyk, M. Kurdziel, P. I. Wójcik, Encouraging orthogonality between weight vectors in pretrained deep neural networks, Neurocomputing 202 (2016) 84–90

2016

Later among the works it cites.

D. Marcos, M. Volpi, D. Tuia, Learning rotation invariant convolutional filters for texture classification, in: Pattern Recognition (ICPR), 2016 23rd International Conference on, IEEE, 2016, pp. 2012–2017

2017

Closest in time.

J. Yu, C. Hong, Y. Rui, D. Tao, Multi-task autoencoder model for recovering human poses, IEEE Transactions on Industrial Electronics PP (99) (2017) 1–1

2017

Closest in time.