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Leveraging prior knowledge on intraclass variance due to transformations is a powerful method to improve the sample complexity of deep neural networks.
Scale steerable filters for locally scale-invariant convolutional neural networks
Ghosh, R. and Gupta, A. K. (2019) · 1906
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Scale-equivariant neural networks with decomposed convolutional filters
Zhu, W., Qiu, Q., Calderbank, A. R., Sapiro, G., and Cheng, X. (2019) · 1909
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Diaconu, N. and Worrall, D. E. (2019a) · 1911
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Der endlichkeitssatz der invarianten endlicher gruppen
Noether, E. (1916) · 1916
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The design and use of steerable filters
Freeman, W. T. and Adelson, E. H. (1991) · 1991
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On the existence of complete invariant feature spaces in pattern recognition
Schulz-Mirbach, H. (1992) · 1992
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Algorithms for the construction of invariant features
Schulz-Mirbach, H. (1994) · 1994
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Invariant features for gray scale images
Schulz-Mirbach, H. (1995) · 1995
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Invariant features for 3d-data based on group integration using directional information and spherical harmonic expansion
Reisert, M. and Burkhardt, H. (2006) · 2006
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An empirical evaluation of deep architectures on problems with many factors of variation
Larochelle, H., Erhan, D., Courville, A. C., Bergstra, J., and Bengio, Y. (2007) · 2007
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Learning multiple layers of features from tiny images,
Krizhevsky, A. (2009) · 2009
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Invariant-integration method for robust feature extraction in speaker-independent speech recognition
Müller, F. and Mertins, A. (2009) · 2009
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Invariant integration features combined with speaker-adaptation methods
Müller, F. and Mertins, A. (2010) · 2010
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Group equivariant stand-alone self-attention for vision
Romero, D. W. and Cordonnier, J. (2020) · 2010
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Trajectory prediction using equivariant continuous convolution
Walters, R., Li, J., and Yu, R. (2020) · 2010
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A. Y., and Lee, H. (2011) · 2011
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Contextual invariant-integration features for improved speaker-independent speech recognition
Müller, F. and Mertins, A. (2011) · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. (2011) · 2011
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Sparse representations and invariant sequence-feature extraction for event detection
Condurache, A. P. and Mertins, A. (2012) · 2012
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Lietransformer: Equivariant self-attention for lie groups
Hutchinson, M., Lan, C. L., Zaidi, S., Dupont, E., Teh, Y. W., and Kim, H. (2020) · 2012
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Locally scale-invariant convolutional neural networks
Kanazawa, A., Sharma, A., and Jacobs, D. W. (2014) · 2014
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Scale-invariant convolutional neural networks
Xu, Y., Xiao, T., Zhang, J., Yang, K., and Zhang, Z. (2014) · 2014
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W. J. (2015) · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. E. (2015) · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M. (2016) · 2016
Learning steerable filters for rotation equivariant cnns
Weiler, M., Hamprecht, F. A., and Storath, M. (2018b) · 2018
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Cubenet: Equivariance to 3d rotation and translation
Worrall, D. E. and Brostow, G. J. (2018) · 2018
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Gauge equivariant convolutional networks and the icosahedral CNN
Cohen, T., Weiler, M., Kicanaoglu, B., and Welling, M. (2019a) · 2019
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A general theory of equivariant cnns on homogeneous spaces
Cohen, T. S., Geiger, M., and Weiler, M. (2019b) · 2019
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Learning to convolve: A generalized weight-tying approach
Diaconu, N. and Worrall, D. E. (2019b) · 2019
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Snip: single-shot network pruning based on connection sensitivity
Lee, N., Ajanthan, T., and Torr, P. H. S. (2019) · 2019
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TI-POOLING: transformation-invariant pooling for feature learning in convolutional neural networks
Laptev, D., Savinov, N., Buhmann, J. M., and Pollefeys, M. (2016) · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N. (2016) · 2016
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Improved regularization of convolutional neural networks with cutout
Devries, T. and Taylor, G. W. (2017) · 2017
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Rotation equivariant vector field networks
Marcos, D., Volpi, M., Komodakis, N., and Tuia, D. (2017) · 2017
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Harmonic networks: Deep translation and rotation equivariance
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J. (2017) · 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. J., Pluim, J. P. W., and Duits, R. (2018) · 2018
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Equivariant transformer networks
Tai, K. S., Bailis, P., and Valiant, G. (2019) · 2019
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General e(2)-equivariant steerable cnns
Weiler, M. and Cesa, G. (2019) · 2019
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Pulmonary nodule detection in CT scans with equivariant cnns
Winkels, M. and Cohen, T. S. (2019) · 2019
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Deep scale-spaces: Equivariance over scale
Worrall, D. E. and Welling, M. (2019) · 2019
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Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Yang, F., Wang, Z., and Heinze-Deml, C. (2019) · 2019
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B-spline cnns on lie groups
Bekkers, E. J. (2020) · 2020
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Se(3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F., Worrall, D. E., Fischer, V., and Welling, M. (2020) · 2020
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Invariant integration in deep convolutional feature space
Rath, M. and Condurache, A. P. (2020) · 2020
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Attentive group equivariant convolutional networks
Romero, D. W., Bekkers, E. J., Tomczak, J. M., and Hoogendoorn, M. (2020) · 2020
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Co-attentive equivariant neural networks: Focusing equivariance on transformations co-occurring in data
Romero, D. W. and Hoogendoorn, M. (2020) · 2020
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Scale-equivariant steerable networks
Sosnovik, I., Szmaja, M., and Smeulders, A. W. M. (2020) · 2020
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A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups
Finzi, M., Welling, M., and Wilson, A. G. (2021) · 2021
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Fuchs, F. B., Wagstaff, E., Dauparas, J., and Posner, I. (2021) · 2021
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015) · 2025
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