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In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size.
R. V. Hartley. A more symmetrical fourier analysis applied to transmission problems. Proceedings of the IRE
1942
Earlier work this paper cites.
D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology
1962
Earlier work this paper cites.
R. N. Bracewell. Discrete Hartley transform. J. Opt. Soc. Am
1983
Earlier work this paper cites.
R. N. Bracewell. The fast hartley transform. Proceedings of the IEEE
1984
Earlier work this paper cites.
R. N. Bracewell. The Hartley transform. Oxford University Press, Inc
1986
Earlier work this paper cites.
J. Agbinya. Fast interpolation algorithm using fast hartley transform. Proceedings of the IEEE
1987
Earlier work this paper cites.
Y. LeCun, B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, and L. D. Jackel. Handwritten digit recognition with a backpropagation network. In Advances in Neural Information Processing Systems
1990
Earlier work this paper cites.
R. Millane. Analytic properties of the hartley transform and their implications. Proceedings of the IEEE
1994
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proc. IEEE
1998
Earlier work this paper cites.
A. Torralba and A. Oliva. Statistics of natural image categories. Network: Computation in Neural Systems
2003
Earlier work this paper cites.
E. Kussul and T. Baidyk. Improved method of handwritten digit recognition tested on mnist database. Image and Vision Computing
2004
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems
2012
Earlier work this paper cites.
J. G. Proakis and D. G. Manolakis. Digital signal processing. Pearson Education
2013
Earlier work this paper cites.
M. D. Zeiler and R. Fergus. Stochastic pooling for regularization of deep convolutional neural networks. In International Conference on Learning Representations
2013
Cited alongside, same era.
C. Gulcehre, K. Cho, R. Pascanu, and Y. Bengio. Learned-norm pooling for deep feedforward and recurrent neural networks. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases
2014
Cited alongside, same era.
J. Bruna, A. Szlam, and Y. LeCun. Signal recovery from pooling representations. In International Conference on Machine Learning
2014
Cited alongside, same era.
D. Yu, H. Wang, P. Chen, and Z. Wei. Mixed pooling for convolutional neural networks. In International Conference on Rough Sets and Knowledge Technology
2014
Cited alongside, same era.
B. Graham. Fractional max-pooling. arXiv preprint arXiv:1412.6071
R. K. Srivastava, K. Greff, and J. Schmidhuber. Highway networks. In International Conference on Machine Learning Deep learning workshops
2015
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Later among the works it cites.
C.-Y. Lee, P. W. Gallagher, and Z. Tu. Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree. Artificial Intelligence and Statistics
2016
Later among the works it cites.
S. Zhai, H. Wu, A. Kumar, Y. Cheng, Y. Lu, Z. Zhang, and R. Feris. S3pool: Pooling with stochastic spatial sampling. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2017
Later among the works it cites.
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2014
Cited alongside, same era.
O. Rippel, J. Snoek, and R. P. Adams. Spectral representations for convolutional neural networks. In Advances in Neural Information Processing Systems
2015
Cited alongside, same era.
J. Schmidhuber. Deep learning in neural networks: An overview. Neural Networks
2015
Cited alongside, same era.
Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature
2015
Cited alongside, same era.
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations
2015
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich et al.. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Cited alongside, same era.
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller. Striving for simplicity: The all convolutional net. In International Conference on Learning Representations
2015
Cited alongside, same era.
D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations
2015
Cited alongside, same era.
2017
Later among the works it cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. Automatic differentiation in pytorch. In Advances in Neural Information Processing Systems
2017
Later among the works it cites.
Sun, Manli, Zhanjie Song, Xiaoheng Jiang, Jing Pan, and Yanwei Pang. Learning pooling for convolutional neural network. Neurocomputing
2017
Later among the works it cites.
2017
Later among the works it cites.
Saeedan, Faraz, Nicolas Weber, Michael Goesele, and Stefan Roth. Detail-preserving pooling in deep networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Closest in time.
Smith, James S., and Bogdan M. Wilamowski. Discrete cosine transform spectral pooling layers for convolutional neural networks. In International Conference on Artificial Intelligence and Soft Computing
2018
Closest in time.
Travis Williams and Robert Li. Wavelet pooling for convolutional neural networks. In International Conference on Learning Representations
2018
Closest in time.
Akhtar, N., Ragavendran, U. Interpretation of intelligence in CNN-pooling processes: a methodological survey. Neural Comput. & Applic
2020
Closest in time.