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Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years.
Scale-space and edge detection using anisotropic diffusion
Perona, P., Malik, J.: · 1990
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Nonlinear total variation based noise removal algorithms
Rudin, L.I., Osher, S., Fatemi, E.: · 1992
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Deformable templates using large deformation kinematics
Christensen, G.E., Rabbitt, R.D., Miller, M.I.: · 1996
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Image matching as a diffusion process: an analogy with maxwell’s demons
Thirion, J.P.: · 1998
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Flows of diffeomorphisms for multimodal image registration
Chefd’Hotel, C., Hermosillo, G., Faugeras, O.: · 2002
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A unified approach to fast image registration and a new curvature based registration technique
Fischer, B., Modersitzki, J.: · 2004
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Sobolev active contours
Sundaramoorthi, G., Yezzi, A., Mennucci, A.C.: · 2007
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Visualizing data using t-sne
Maaten, L.v.d., Hinton, G.: · 2008
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Diffeomorphic demons: Efficient non-parametric image registration
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: · 2009
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Sobolev gradients and differential equations
Neuberger, J.: · 2009
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Visualizing higher-layer features of a deep network
Erhan, D., Bengio, Y., Courville, A., Vincent, P.: · 2009
Earlier work this paper cites.
Image sharpening via sobolev gradient flows
Calder, J., Mansouri, A., Yezzi, A.: · 2010
Earlier work this paper cites.
An introduction to total variation for image analysis
Chambolle, A., Caselles, V., Cremers, D., Novaga, M., Pock, T.: · 2010
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., Zisserman, A.: · 2013
Cited alongside, same era.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: · 2013
Cited alongside, same era.
Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
Cited alongside, same era.
Striving for simplicity: The all convolutional net
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: · 2014
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., Clune, J.: · 2015
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Fong, R.C., Vedaldi, A.: · 2017
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Patternnet and patternlrp–improving the interpretability of neural networks
Kindermans, P.J., Schütt, K.T., Alber, M., Müller, K.R., Dähne, S.: · 2017
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The (un) reliability of saliency methods
Kindermans, P.J., Hooker, S., Adebayo, J., Alber, M., Schütt, K.T., Dähne, S., Erhan, D., Kim, B.: · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., Yan, Q.: · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: · 2017
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Inverting convolutional networks with convolutional networks
Dosovitskiy, A., Brox, T.: · 2015
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: · 2016
Cited alongside, same era.
Visualizing deep convolutional neural networks using natural pre-images
Mahendran, A., Vedaldi, A.: · 2016
Cited alongside, same era.
Plug & play generative networks: Conditional iterative generation of images in latent space
Nguyen, A., Yosinski, J., Bengio, Y., Dosovitskiy, A., Clune, J.: · 2016
Cited alongside, same era.
A taxonomy and library for visualizing learned features in convolutional neural networks
Grün, F., Rupprecht, C., Navab, N., Tombari, F.: · 2016
Cited alongside, same era.
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Later among the works it cites.
Regularization for deep learning: A taxonomy
Kukačka, J., Golkov, V., Cremers, D.: · 2017
Later among the works it cites.
The building blocks of interpretability
Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., Mordvintsev, A.: · 2018
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Visualizing googlenet classes
Øygard, A.M.: · 2018
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Inceptionism: Going deeper into neural networks
Mordvintsev, A., O.C.T.M.: · 2018
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Class visualization with bilateral filters
Tyka, M.: · 2018
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Deepdreaming with tensorflow
et al., M.: · 2018
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A practical test for univariate and multivariate normality
Dmitry Ulyanov, Andrea Vedaldi, V.L.: · 2018
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