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In this paper, we consider adversarial attacks against a system of monocular depth estimation (MDE) based on convolutional neural networks (CNNs).
Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
Bo Li, Chunhua Shen, Yuchao Dai, Anton van den Hengel, and Mingyi He · 2015
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Deeper depth prediction with fully convolutional residual networks
Laina Iro, Rupprecht Christian, Belagiannis Vasileios, Tombari Federico, and Navab Nassir · 2016
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Salient deconvolutional networks
Aravindh Mahendran and Andrea Vedaldi · 2016
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Parseval networks: Improving robustness to adversarial examples
Moustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Magnet: A two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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Biologically inspired protection of deep networks from adversarial attacks
Aran Nayebi and Surya Ganguli · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
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Sparse-to-dense: Depth prediction from sparse depth samples and a single image
Fangchang Ma and Sertac Karaman · 2018
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Cascade adversarial machine learning regularized with a unified embedding
Taesik Na, Jong Hwan Ko, and Saibal Mukhopadhyay · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel · 2018
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How do neural networks see depth in single images?
Tom van Dijk and Guido de Croon · 2019
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Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries
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Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Fisher Yu, Vladlen Koltun, and Thomas Funkhouser · 2017
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Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal S. Mian · 2018
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jianfeng Zhu, and Xiaolin C. Hu · 2018
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Junjie Hu, Mete Ozay, Yan Zhang, and Takayuki Okatani · 2019
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Visualization of convolutional neural networks for monocular depth estimation
Junjie Hu, Yan Zhang, and Takayuki Okatani · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Adv-bnn: Improved adversarial defense through robust bayesian neural network
Xuanqing Liu, Yao Li, Chongruo Wu, and Cho-Jui Hsieh · 2019
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