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Group convolutional neural networks (G-CNNs) have been shown to increase parameter efficiency and model accuracy by incorporating geometric inductive biases.
Perceptrons: expanded edition, 1988
Minsky, M. L. and Papert, S. A · 1988
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Group theoretical methods in machine learning
Kondor, I. R · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Learning transformation groups and their invariants
Cohen, T · 2013
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Lin, M., Chen, Q., and Yan, S · 2013
Earlier work this paper cites.
Learning separable filters
Rigamonti, R., Sironi, A., Lepetit, V., and Fua, P · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Rigid-motion scattering for texture classification
Sifre, L. and Mallat, S · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Template matching via densities on the roto-translation group
Bekkers, E. J., Loog, M., ter Haar Romeny, B. M., and Duits, R · 2017
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Geometric deep learning: going beyond euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2017
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Xception: Deep learning with depthwise separable convolutions
Chollet, F · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
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Rotation equivariant vector field networks
Marcos, D., Volpi, M., Komodakis, N., and Tuia, D · 2017
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Harmonic networks: Deep translation and rotation equivariance
Deep learning of multi-element abundances from high-resolution spectroscopic data
Leung, H. W. and Bovy, J · 2019
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Red-nn: Rotation-equivariant deep neural network for classification and prediction of rotation
Salas, R. R., Dokládal, P., and Dokladalova, E · 2019
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Scale-equivariant steerable networks
Sosnovik, I., Szmaja, M., and Smeulders, A · 2019
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General e ( 2 ) e(2) -equivariant steerable cnns
Weiler, M. and Cesa, G · 2019
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Deep scale-spaces: Equivariance over scale
Worrall, D. E. and Welling, M · 2019
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Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J · 2017
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Roto-translation covariant convolutional networks for medical image analysis
Bekkers, E. J., Lafarge, M. W., Veta, M., Eppenhof, K. A., Pluim, J. P., and Duits, R · 2018
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Learning so (3) equivariant representations with spherical cnns
Esteves, C., Allen-Blanchette, C., Makadia, A., and Daniilidis, K · 2018
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Sample efficient semantic segmentation using rotation equivariant convolutional networks
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3d g-cnns for pulmonary nodule detection
Winkels, M. and Cohen, T. S · 2018
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Cubenet: Equivariance to 3d rotation and translation
Worrall, D. E. and Brostow, G · 2018
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B-spline cnns on lie groups
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Pointconv: Deep convolutional networks on 3d point clouds
Wu, W., Qi, Z., and Fuxin, L · 2019
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Finzi, M., Stanton, S., Izmailov, P., and Wilson, A. G · 2020
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Rethinking depthwise separable convolutions: How intra-kernel correlations lead to improved mobilenets
Haase, D. and Amthor, M · 2020
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Wavelet networks: Scale equivariant learning from raw waveforms
Romero, D. W., Bekkers, E. J., Tomczak, J. M., and Hoogendoorn, M · 2020
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Implicit neural representations with periodic activation functions
Sitzmann, V., Martel, J. N., Bergman, A. W., Lindell, D. B., and Wetzstein, G · 2020
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Exploiting learned symmetries in group equivariant convolutions
Lengyel, A. and van Gemert, J. C · 2021
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Ckconv: Continuous kernel convolution for sequential data
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