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Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models.
Linear programming and extensions
G. Dantzig · 1963
Earlier work this paper cites.
Inversion of feedforward neural networks: algorithms and applications
C. A. Jensen, R. D. Reed, R. J. Marks, M. A. El-Sharkawi, J.-B. Jung, R. T. Miyamoto, G. M. Anderson, and C. J. Eggen · 1999
Earlier work this paper cites.
Inverting feedforward neural networks using linear and nonlinear programming
B.-L. Lu, H. Kita, and Y. Nishikawa · 1999
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
MNIST handwritten digit database, 2010
Y. LeCun and C. Cortes · 2010
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
Earlier work this paper cites.
Signal recovery from pooling representations
J. Bruna, A. Szlam, and Y. LeCun · 2014
Earlier work this paper cites.
On the number of linear regions of deep neural networks
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Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
Why are deep nets reversible: a simple theory, with implications for training
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Earlier work this paper cites.
The loss surfaces of multilayer networks
A. Choromanska, M. Henaff, M. Mathieu, G. Ben Arous, and Y. LeCun · 2015
Earlier work this paper cites.
Adam: a method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2016
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Inverting convolutional networks with convolutional networks
A. Dosovitskiy and T. Brox · 2016
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Visualizing deep convolutional neural networks using natural pre-images
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Improving training of deep neural networks via singular value bounding
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On the expressive power of deep neural networks
M. Raghu, B. Poole, J. Kleinberg, S. Ganguli, and J. Sohl-Dickstein · 2017
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Regularizing cnns with locally constrained decorrelations
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Dgm: a deep learning algorithm for solving partial differential equations
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