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The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized.
GAN dissection: Visualizing and understanding generative adversarial networks
Bau, D., Zhu, J., Strobelt, H., Zhou, B., Tenenbaum, J.B., Freeman, W.T., Torralba, A., 2018 · 1901
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Parametric fuzzy sets for automatic color naming
Benavente, R., Vanrell, M., Baldrich, R., 2008 · 2008
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ImageNet: A Large-Scale Hierarchical Image Database, in: Proc. CVPR
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
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Deconvolutional networks, in: Proc. CVPR
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R., 2010 · 2010
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Visual Population Codes - Toward a Common Multivariate Framework for Cell Recording and Functional Imaging
Kriegeskorte, N., Kreiman, G., 2011 · 2011
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Kernel analysis of deep networks
Montavon, G., Braun, M., Müller, K.R., 2011 · 2011
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Color in the cortex: Single- and double-opponent cells
Shapley, R., Hawken, M., 2011 · 2011
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Deep neural networks rival the representation of primate it cortex for core visual object recognition
Cadieu, C.F., Hong, H., Yamins, D.L.K., Pinto, N., Ardila, D., Solomon, E.A., Majaj, N.J., DiCarlo, J.J., 2014 · 2014
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Return of the devil in the details: Delving deep into convolutional nets
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A., 2014 · 2014
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Weighted principal component analysis: a weighted covariance eigendecomposition approach
Delchambre, L., 2014 · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I.J., Shlens, J., Szegedy, C., 2014 · 2014
Cited alongside, same era.
Why does deep learning work? - A perspective from group theory
Paul, A., Venkatasubramanian, S., 2014 · 2014
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps, in: Int. Conf. on Learning Representations
Simonyan, K., Vedaldi, A., Zisserman, A., 2014 · 2014
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Visualizing and understanding convolutional networks, in: In Proc. of ECCV
Zeiler, M.D., Fergus, R., 2014 · 2014
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Understanding deep features with computer-generated imagery, in: Proc. of ICCV
Aubry, M., Russell, B.C., 2015 · 2015
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Convergent learning: Do different neural networks learn the same representations?, in: Int. Conf. on Learning Representations
Li, Y., Yosinski, J., Clune, J., Lipson, H., Hopcroft, J.E., 2016 · 2016
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Distilling a neural network into a soft decision tree
Frosst, N., Hinton, G., 2017 · 2017
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Feature visualization
Olah, C., Mordvintsev, A., Schubert, L., 2017 · 2017
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Color representation in cnns: parallelisms with biological vision, in: Int. Conference on Computer Vision (Workshops)
Rafegas, I., Vanrell, M., 2017 · 2017
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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks, in: Proc. CVPR
Fong, R., Vedaldi, A., 2018 · 2018
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Learning to generate chairs with convolutional neural networks, in: Proc. CVPR
Dosovitskiy, A., Springenberg, J., Brox, T., 2015 · 2015
Cited alongside, same era.
Understanding deep image representations by inverting them
Mahendran, A., Vedaldi, A., 2015 · 2015
Cited alongside, same era.
Matconvnet – convolutional neural networks for matlab, in: Proceeding of the ACM Int. Conf. on Multimedia
Vedaldi, A., Lenc, K., 2015 · 2015
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Understanding neural networks through deep visualization, in: Int. Conf. on ML
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H., 2015 · 2015
Cited alongside, same era.
Inverting visual representations with convolutional networks
Dosovitskiy, A., Brox, T., 2016 · 2016
Cited alongside, same era.
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks, in: NIPS’16, pp. 3395–3403
Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J., 2016a
Cited in the paper.
Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks
Nguyen, A.M., Yosinski, J., Clune, J., 2016b
Cited in the paper.
The building blocks of interpretability
Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., Mordvintsev, A., 2018 · 2018
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Color encoding in biologically inspired convolutional neural networks
Rafegas, I., Vanrell, M., 2018 · 2018
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Interpreting deep visual representations via network dissection
Zhou, B., Bau, D., Oliva, A., Torralba, A., 2018 · 2018
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Revisiting the importance of individual units in cnns via ablation
Zhou, B., Sun, Y., Bau, D., Torralba, A., 2018 · 2018
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