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Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets.
arXiv preprint arXiv:1905.02175
Ilyas A, et al. (2019) Adversarial examples are not bugs, they are features · 1905
Earlier work this paper cites.
The handbook of brain theory and neural networks
LeCun Y, Bengio Y, , et al. (1995) Convolutional networks for images, speech, and time series · 1995
Earlier work this paper cites.
Deng J, et al. (2009) Imagenet: A large-scale hierarchical image database in CVPR
2009
Earlier work this paper cites.
IEEE Transactions on Image Processing
Van De Weijer J, Schmid C, Verbeek J, Larlus D (2009) Learning color names for real-world applications · 2009
Earlier work this paper cites.
pp. 1097–1105
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks in NeurIPS · 2012
Earlier work this paper cites.
IEEE transactions on pattern analysis and machine intelligence
Bengio Y, Courville A, Vincent P (2013) Representation learning: A review and new perspectives · 2013
Earlier work this paper cites.
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks in ECCV
2014
Earlier work this paper cites.
Goodfellow I, et al. (2014) Generative adversarial nets in NeurIPS
2014
Earlier work this paper cites.
Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database in NeurIPS
2014
Earlier work this paper cites.
Szegedy C, et al. (2014) Intriguing properties of neural networks in ICLR
2014
Earlier work this paper cites.
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2015) Object detectors emerge in deep scene cnns in ICLR
2015
Earlier work this paper cites.
Mahendran A, Vedaldi A (2015) Understanding deep image representations by inverting them in CVPR
2015
Earlier work this paper cites.
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition in ICLR
2015
Earlier work this paper cites.
pp. 3156–3164
Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: A neural image caption generator in CVPR · 2015
Earlier work this paper cites.
PloS one
Bach S, et al. (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation · 2015
Earlier work this paper cites.
arXiv preprint arXiv:1506.03365
Yu F, et al. (2015) Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop · 2015
Earlier work this paper cites.
Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples in ICLR
2015
Earlier work this paper cites.
Karpathy A, Johnson J, Fei-Fei L (2016) Visualizing and understanding recurrent networks in ICLR
2016
Cited alongside, same era.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition in CVPR
2016
Cited alongside, same era.
Zhou T, Krahenbuhl P, Aubry M, Huang Q, Efros AA (2016) Learning dense correspondence via 3d-guided cycle consistency in CVPR
2016
Cited alongside, same era.
(ACM), pp. 1135–1144
Ribeiro MT, Singh S, Guestrin C (2016) Why should i trust you?: Explaining the predictions of any classifier in SIGKDD · 2016
Cited alongside, same era.
(Springer), pp. 3–19
Hendricks LA, et al. (2016) Generating visual explanations in ECCV · 2016
Cited alongside, same era.
Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks in ICLR
Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2017) Pruning filters for efficient convnets in ICLR
2017
Later among the works it cites.
(IEEE), pp. 39–57
Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks in 2017 IEEE Symposium on Security and Privacy (SP) · 2017
Later among the works it cites.
arXiv preprint arXiv:1707.04131
Rauber J, Brendel W, Bethge M (2017) Foolbox: A python toolbox to benchmark the robustness of machine learning models · 2017
Later among the works it cites.
Zhou B, et al. (2017) Scene parsing through ade20k dataset in CVPR
2017
Later among the works it cites.
Bau A, et al. (2018) Identifying and controlling important neurons in neural machine translation in NeurIPS
2018
Later among the works it cites.
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2016
Cited alongside, same era.
Zhu JY, Krähenbühl P, Shechtman E, Efros AA (2016) Generative visual manipulation on the natural image manifold in ECCV
2016
Cited alongside, same era.
pp. 2172–2180
Chen X, et al. (2016) Infogan: Interpretable representation learning by information maximizing generative adversarial nets in NeurIPS · 2016
Cited alongside, same era.
arXiv preprint arXiv:1704.01444
Radford A, Jozefowicz R, Sutskever I (2017) Learning to generate reviews and discovering sentiment · 2017
Cited alongside, same era.
Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks in CVPR
2017
Cited alongside, same era.
Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks in ICCV
2017
Cited alongside, same era.
pp. 3429–3437
Fong RC, Vedaldi A (2017) Interpretable explanations of black boxes by meaningful perturbation in ICCV · 2017
Cited alongside, same era.
Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of gans for improved quality, stability, and variation in ICLR
2018
Later among the works it cites.
Petsiuk V, Das A, Saenko K (2018) Rise: Randomized input sampling for explanation of black-box models in BMVC
2018
Later among the works it cites.
Xiao T, Liu Y, Zhou B, Jiang Y, Sun J (2018) Unified perceptual parsing for scene understanding in ECCV
2018
Later among the works it cites.
arXiv preprint arXiv:1811.12231
Geirhos R, et al. (2018) Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness · 2018
Later among the works it cites.
arXiv preprint arXiv:1803.06959
Morcos AS, Barrett DG, Rabinowitz NC, Botvinick M (2018) On the importance of single directions for generalization · 2018
Later among the works it cites.
arXiv preprint arXiv:1806.02891
Zhou B, Sun Y, Bau D, Torralba A (2018) Revisiting the importance of individual units in cnns via ablation · 2018
Later among the works it cites.
Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2018) Towards deep learning models resistant to adversarial attacks in ICLR
2018
Later among the works it cites.
Zhang Q, Nian Wu Y, Zhu SC (2018) Interpretable convolutional neural networks in CVPR
2018
Later among the works it cites.
Bau D, et al. (2019) Gan dissection: Visualizing and understanding generative adversarial networks in ICLR
2019
Later among the works it cites.
Koul A, Fern A, Greydanus S (2019) Learning finite state representations of recurrent policy networks in ICLR
2019
Later among the works it cites.
pp. 2074–2082
Wen W, Wu C, Wang Y, Chen Y, Li H (2016) Learning structured sparsity in deep neural networks in NeurIPS · 2082
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