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In this paper, we investigate properties and limitations of invariance learned by neural networks from the data compared to the genuine invariance achieved through invariant weight-tying.
Efficient saliency maps for explainable ai
Mundhenk, T. N., Chen, B. Y., and Friedland, G. (2019) · 1911
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LeCun, Y., Haffner, P., Bottou, L., and Bengio, Y. (1999) · 1998
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020) · 2002
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Goodfellow, I., Lee, H., Le, Q., Saxe, A., and Ng, A. (2009) · 2009
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Understanding the difficulty of training deep feedforward neural networks
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He, K., Zhang, X., Ren, S., and Sun, J. (2015) · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M. (2016) · 2016
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The effectiveness of data augmentation in image classification using deep learning
Perez, L. and Wang, J. (2017) · 2017
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Sundararajan, M., Taly, A., and Yan, Q. (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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R. (2017) · 2017
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Spectral norm regularization for improving the generalizability of deep learning
Yoshida, Y. and Miyato, T. (2017) · 2017
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Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R., and Smola, A. (2017) · 2017
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Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V. (2018) · 2018
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Art of singular vectors and universal adversarial perturbations
Khrulkov, V. and Oseledets, I. (2018) · 2018
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Learning invariances in neural networks from training data
Benton, G., Finzi, M., Izmailov, P., and Wilson, A. G. (2020) · 2020
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Naturally occurring equivariance in neural networks
Olah, C., Cammarata, N., Voss, C., Schubert, L., and Goh, G. (2020) · 2020
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Scale-equivariant steerable networks
Sosnovik, I., Szmaja, M., and Smeulders, A. (2020) · 2020
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Meta-learning symmetries by reparameterization
Zhou, A., Knowles, T., and Finn, C. (2020) · 2020
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Automatic symmetry discovery with lie algebra convolutional network
Dehmamy, N., Walters, R., Liu, Y., Wang, D., and Yu, R. (2021) · 2021
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R. (2021) · 2021
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Shorten, C. and Khoshgoftaar, T. M. (2019) · 2019
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General E(2)-Equivariant Steerable CNNs
Weiler, M. and Cesa, G. (2019) · 2019
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Worrall, D. and Welling, M. (2019) · 2019
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B-spline {cnn}s on lie groups
Bekkers, E. J. (2020) · 2020
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Liegg: Studying learned lie group generators
Moskalev, A., Sepliarskaia, A., Sosnovik, I., and Smeulders, A. (2022a)
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Contrasting quadratic assignments for set-based representation learning
Moskalev, A., Sosnovik, I., Volker, F., and Smeulders, A. (2022b)
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
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Steerable partial differential operators for equivariant neural networks
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In what ways are deep neural networks invariant and how should we measure this?
Kvinge, H., Emerson, T., Jorgenson, G., Vasquez, S., Doster, T., and Lew, J. (2022) · 2022
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Bispectral neural networks
Sanborn, S., Shewmake, C. A., Olshausen, B., and Hillar, C. J. (2023) · 2023
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