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Many applications require robustness, or ideally invariance, of neural networks to certain transformations of input data.
3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: · 1920
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Tangent prop-a formalism for specifying selected invariances in an adaptive network
Simard, P., Victorri, B., LeCun, Y., Denker, J.: · 1991
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Computing discrete minimal surfaces and their conjugates
Pinkall, U., Polthier, K.: · 1993
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Orthogonal fourier–mellin moments for invariant pattern recognition
Sheng, Y., Shen, L.: · 1994
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Rotation invariant texture features and their use in automatic script identification
Tan, T.: · 1998
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Rotation and scale invariant texture features using discrete wavelet packet transform
Manthalkar, R., Biswas, P.K., Chatterji, B.N.: · 2003
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A sparse texture representation using local affine regions
Lazebnik, S., Schmid, C., Ponce, J.: · 2005
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Radon transform orientation estimation for rotation invariant texture analysis
Jafari-Khouzani, K., Soltanian-Zadeh, H.: · 2005
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Integral invariants for shape matching
Manay, S., Cremers, D., Hong, B.W., Yezzi, A.J., Soatto, S.: · 2006
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Rotation-invariant neoperceptron
Fasel, B., Gatica-Perez, D.: · 2006
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Two-dimensional polar harmonic transforms for invariant image representation
Yap, P.T., Jiang, X., Chichung Kot, A.: · 2010
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: · 2011
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Functional maps: a flexible representation of maps between shapes
Ovsjanikov, M., Ben-Chen, M., Solomon, J., Butscher, A., Guibas, L.: · 2012
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Invariant scattering convolution networks
Bruna, J., Mallat, S.: · 2013
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Rotation, scaling and deformation invariant scattering for texture discrimination
Sifre, L., Mallat, S.: · 2013
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
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Deep roto-translation scattering for object classification
Oyallon, E., Mallat, S.: · 2015
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: · 2015
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3d shapenets: A deep representation for volumetric shape modeling
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: · 2015
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Improving invariance and equivariance properties of convolutional neural networks
Tensmeyer, C., Martinez, T.: · 2016
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Unsupervised learning of invariant representations
Anselmi, F., Leibo, J.Z., Rosasco, L., Mutch, J., Tacchetti, A., Poggio, T.: · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M., Favaro, P.: · 2016
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Group equivariant convolutional networks
Cohen, T., Welling, M.: · 2016
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Ti-pooling: transformation-invariant pooling for feature learning in convolutional neural networks
Laptev, D., Savinov, N., Buhmann, J.M., Pollefeys, M.: · 2016
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Geometric deep learning on graphs and manifolds using mixture model cnns
Monti, F., Boscaini, D., Masci, J., Rodolá, E., Svoboda, J., Bronstein, M.M.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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A rotation and a translation suffice: Fooling cnns with simple transformations
Engstrom, L., Tsipras, D., Schmidt, L., Madry, A.: · 2017
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Harmonic networks: Deep translation and rotation equivariance
Worrall, D.E., Garbin, S.J., Turmukhambetov, D., Brostow, G.J.: · 2017
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Equivariance through parameter-sharing
Ravanbakhsh, S., Schneider, J., Poczos, B.: · 2017
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Rotation equivariant vector field networks
Marcos, D., Volpi, M., Komodakis, N., Tuia, D.: · 2017
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Learning rotation invariant convolutional filters for texture classification
Marcos, D., Volpi, M., Tuia, D.: · 2017
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Warped convolutions: Efficient invariance to spatial transformations
Henriques, J.F., Vedaldi, A.: · 2017
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O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: · 2017
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Normalized object coordinate space for category-level 6d object pose and size estimation
Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: · 2019
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Equivariant transformer networks
Tai, K.S., Bailis, P., Valiant, G.: · 2019
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Spherical fractal convolutional neural networks for point cloud recognition
Rao, Y., Lu, J., Zhou, J.: · 2019
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3d point capsule networks
Zhao, Y., Birdal, T., Deng, H., Tombari, F.: · 2019
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Meshcnn: A network with an edge
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D.: · 2019
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Operatornet: Recovering 3d shapes from difference operators
Huang, R., Rakotosaona, M.J., Achlioptas, P., Guibas, L., Ovsjanikov, M.: · 2019
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: · 2017
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Deep functional maps: Structured prediction for dense shape correspondences
Litany, O., Remez, T., Rodolà, E., Bronstein, A., Bronstein, M.: · 2017
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Understanding image representations by measuring their equivariance and equivalence
Lenc, K., Vedaldi, A.: · 2018
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Unsupervised representation learning by predicting image rotations
Komodakis, N., Gidaris, S.: · 2018
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Learning invariances using the marginal likelihood
Wilk, M.v.d., Bauer, M., John, S., Hensman, J.: · 2018
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Pointnet/pointnet++ pytorch
Yan, X.: · 2019
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Naturally occurring equivariance in neural networks
Olah, C., Cammarata, N., Voss, C., Schubert, L., Goh, G.: · 2020
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On isometry robustness of deep 3d point cloud models under adversarial attacks
Zhao, Y., Wu, Y., Chen, C., Lim, A.: · 2020
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Learning invariances in neural networks from training data
Benton, G.W., Finzi, M., Izmailov, P., Wilson, A.G.: · 2020
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Learning rotation-invariant representations of point clouds using aligned edge convolutional neural networks
Zhang, J., Yu, M.Y., Vasudevan, R., Johnson-Roberson, M.: · 2020
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Deep positional and relational feature learning for rotation-invariant point cloud analysis
Yu, R., Wei, X., Tombari, F., Sun, J.: · 2020
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Certified defense to image transformations via randomized smoothing
Fischer, M., Baader, M., Vechev, M.: · 2020
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Endowing deep 3d models with rotation invariance based on principal component analysis
Xiao, Z., Lin, H., Li, R., Geng, L., Chao, H., Ding, S.: · 2020
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Caspr: Learning canonical spatiotemporal point cloud representations
Rempe, D., Birdal, T., Zhao, Y., Gojcic, Z., Sridhar, S., Guibas, L.J.: · 2020
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Learning to orient surfaces by self-supervised spherical cnns
Spezialetti, R., Stella, F., Marcon, M., Silva, L., Salti, S., Di Stefano, L.: · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F., Worrall, D., Fischer, V., Welling, M.: · 2020
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Isometric transformation invariant and equivariant graph convolutional networks
Horie, M., Morita, N., Hishinuma, T., Ihara, Y., Mitsume, N.: · 2020
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Diffusion is all you need for learning on surfaces
Sharp, N., Attaiki, S., Crane, K., Ovsjanikov, M.: · 2020
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Deep shells: Unsupervised shape correspondence with optimal transport
Eisenberger, M., Toker, A., Leal-Taixé, L., Cremers, D.: · 2020
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Geometric adversarial attacks and defenses on 3d point clouds
Lang, I., Kotlicki, U., Avidan, S.: · 2021
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Staying in shape: learning invariant shape representations using contrastive learning
Gu, J., Yeung, S.: · 2021
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A closer look at rotation-invariant deep point cloud analysis
Li, F., Fujiwara, K., Okura, F., Matsushita, Y.: · 2021
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Canonical capsules: Self-supervised capsules in canonical pose
Sun, W., Tagliasacchi, A., Deng, B., Sabour, S., Yazdani, S., Hinton, G.E., Yi, K.M.: · 2021
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E(n) equivariant graph neural networks
Satorras, V.G., Hoogeboom, E., Welling, M.: · 2021
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High-performance large-scale image recognition without normalization
Brock, A., De, S., Smith, S.L., Simonyan, K.: · 2021
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Art-point: Improving rotation robustness of point cloud classifiers via adversarial rotation
Wang, R., Yang, Y., Tao, D.: · 2022
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Condor: Self-supervised canonicalization of 3d pose for partial shapes
Sajnani, R., Poulenard, A., Jain, J., Dua, R., Guibas, L.J., Sridhar, S.: · 2022
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