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Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output.
Certain topics in telegraph transmission theory
Nyquist, H · 1928
Earlier work this paper cites.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H. and Wiesel, T. N · 1962
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition
Fukushima, K. and Miyake, S · 1982
Earlier work this paper cites.
Pyramid methods in image processing
Adelson, E. H., Anderson, C. H., Bergen, J. R., Burt, P. J., and Ogden, J. M · 1984
Earlier work this paper cites.
The laplacian pyramid as a compact image code
Burt, P. J. and Adelson, E. H · 1987
Earlier work this paper cites.
Handwritten digit recognition with a back-propagation network
LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., and Jackel, L. D · 1990
Earlier work this paper cites.
Digital Image Processing
Gonzalez, R. C. and Woods, R. E · 1992
Earlier work this paper cites.
Shiftable multiscale transforms
Simoncelli, E. P., Freeman, W. T., Adelson, E. H., and Heeger, D. J · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Object recognition from local scale-invariant features
Lowe, D. G · 1999
Earlier work this paper cites.
Discrete-Time Signal Processing
Oppenheim, A. V., Schafer, R. W., and Buck, J. R · 1999
Earlier work this paper cites.
Representing and recognizing the visual appearance of materials using three-dimensional textons
Leung, T. and Malik, J · 2001
Earlier work this paper cites.
The redundant discrete wavelet transform and additive noise
Fowler, J. E · 2005
Earlier work this paper cites.
VLFeat: An open and portable library of computer vision algorithms
Vedaldi, A. and Fulkerson, B · 2008
Earlier work this paper cites.
Measuring invariances in deep networks
Goodfellow, I., Lee, H., Le, Q. V., Saxe, A., and Ng, A. Y · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Evaluation of pooling operations in convolutional architectures for object recognition
Scherer, D., Muller, A., and Behnke, S · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Invariant scattering convolution networks
Bruna, J. and Mallat, S · 2013
Earlier work this paper cites.
Rotation, scaling and deformation invariant scattering for texture discrimination
Sifre, L. and Mallat, S · 2013
Earlier work this paper cites.
Spatial pattern templates for recognition of objects with regular structure
Tyleček, R. and Šára, R · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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Locally scale-invariant convolutional neural networks
Kanazawa, A., Sharma, A., and Jacobs, D · 2014
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Convolutional kernel networks
Mairal, J., Koniusz, P., Harchaoui, Z., and Schmid, C · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Understanding deep features with computer-generated imagery
Aubry, M. and Russell, B. C · 2015
Cited alongside, same era.
Deconvolution and checkerboard artifacts
Odena, A., Dumoulin, V., and Olah, C · 2016
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Multi-scale context aggregation by dilated convolutions
Yu, F. and Koltun, V · 2016
Later among the works it cites.
Invariance and stability of deep convolutional representations
Bietti, A. and Mairal, J · 2017
Later among the works it cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
Later among the works it cites.
Plug & play generative networks: Conditional iterative generation of images in latent space
Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., and Yosinski, J · 2017
Later among the works it cites.
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L · 2015
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Manitest: Are classifiers really invariant?
Fawzi, A. and Frossard, P · 2015
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Skip-thought vectors
Kiros, R., Zhu, Y., Salakhutdinov, R. R., Zemel, R., Urtasun, R., Torralba, A., and Fidler, S · 2015
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Understanding image representations by measuring their equivariance and equivalence
Lenc, K. and Vedaldi, A · 2015
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Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
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Deepdream-a code example for visualizing neural networks
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 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
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Dilated residual networks
Yu, F., Koltun, V., and Funkhouser, T · 2017
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Why do deep convolutional networks generalize so poorly to small image transformations?
Azulay, A. and Weiss, Y · 2018
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L · 2018
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Polar transformer networks
Esteves, C., Allen-Blanchette, C., Zhou, X., and Daniilidis, K · 2018
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Pooling is neither necessary nor sufficient for appropriate deformation stability in cnns
Ruderman, A., Rabinowitz, N. C., Morcos, A. S., and Zoran, D · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
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Spatially transformed adversarial examples
Xiao, C., Zhu, J.-Y., Li, B., He, W., Liu, M., and Song, D · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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A rotation and a translation suffice: Fooling cnns with simple transformations
Engstrom, L., Tsipras, D., Schmidt, L., and Madry, A · 2019
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Using pre-training can improve model robustness and uncertainty
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A style-based generator architecture for generative adversarial networks
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One pixel attack for fooling deep neural networks
Su, J., Vargas, D. V., and Sakurai, K · 2019
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