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Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks.
Branched multi-task networks: deciding what layers to share
Vandenhende, S.; Georgoulis, S.; De Brabandere, B.; and Van Gool, L. 2019 · 1904
Earlier work this paper cites.
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique
Ghamizi, S.; Cordy, M.; Papadakis, M.; and Traon, Y. L. 2019 · 1912
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A survey of transfer and multitask learning in bioinformatics
Xu, Q.; and Yang, Q. 2011 · 2011
Earlier work this paper cites.
Poisoning attacks against support vector machines
Biggio, B.; Nelson, B.; and Laskov, P. 2012 · 2012
Earlier work this paper cites.
Evasion attacks against machine learning at test time
Biggio, B.; Corona, I.; Maiorca, D.; Nelson, B.; Šrndić, N.; Laskov, P.; Giacinto, G.; and Roli, F. 2013 · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2013 · 2013
Earlier work this paper cites.
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2016 · 2016
Earlier work this paper cites.
Adversarial example defense: Ensembles of weak defenses are not strong
He, W.; Wei, J.; Chen, X.; Carlini, N.; and Song, D. 2017 · 2017
Earlier work this paper cites.
On detecting adversarial perturbations
Metzen, J. H.; Genewein, T.; Fischer, V.; and Bischoff, B. 2017 · 2017
Earlier work this paper cites.
Certifying some distributional robustness with principled adversarial training
Sinha, A.; Namkoong, H.; Volpi, R.; and Duchi, J. 2017 · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Tramèr, F.; Papernot, N.; Goodfellow, I.; Boneh, D.; and McDaniel, P. 2017 · 2017
Cited alongside, same era.
A survey on multi-task learning
Zhang, Y.; and Yang, Q. 2017 · 2017
Cited alongside, same era.
On the robustness of semantic segmentation models to adversarial attacks
Arnab, A.; Miksik, O.; and Torr, P. H. 2018 · 2018
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Feature space perturbations yield more transferable adversarial examples
Inkawhich, N.; Wen, W.; Li, H. H.; and Chen, Y. 2019 · 2019
Later among the works it cites.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2019 · 2019
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Habitat: A platform for embodied ai research
Savva, M.; Kadian, A.; Maksymets, O.; Zhao, Y.; Wijmans, E.; Jain, B.; Straub, J.; Liu, J.; Koltun, V.; Malik, J.; et al. 2019 · 2019
Later among the works it cites.
First-order adversarial vulnerability of neural networks and input dimension
Simon-Gabriel, C.-J.; Ollivier, Y.; Bottou, L.; Schölkopf, B.; and Lopez-Paz, D. 2019 · 2019
Later among the works it cites.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F.; and Hein, M. 2020 · 2020
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Athalye, A.; Carlini, N.; and Wagner, D. 2018 · 2018
Cited alongside, same era.
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Chen, Z.; Badrinarayanan, V.; Lee, C.-Y.; and Rabinovich, A. 2018 · 2018
Cited alongside, same era.
Boosting adversarial attacks with momentum
Dong, Y.; Liao, F.; Pang, T.; Su, H.; Zhu, J.; Hu, X.; and Li, J. 2018 · 2018
Cited alongside, same era.
Vlocnet++: Deep multitask learning for semantic visual localization and odometry
Radwan, N.; Valada, A.; and Burgard, W. 2018 · 2018
Cited alongside, same era.
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies
Sax, A.; Emi, B.; Zamir, A. R.; Guibas, L. J.; Savarese, S.; and Malik, J. 2018 · 2018
Cited alongside, same era.
Taskonomy: Disentangling Task Transfer Learning
Zamir, A. R.; Sax, A.; Shen, W. B.; Guibas, L. J.; Malik, J.; and Savarese, S. 2018 · 2018
Cited alongside, same era.
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015a
Cited in the paper.
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015b
Cited in the paper.
Ghamizi, S.; Cordy, M.; Gubri, M.; Papadakis, M.; Boystov, A.; Le Traon, Y.; and Goujon, A. 2020 · 2020
Later among the works it cites.
Multitask Learning Strengthens Adversarial Robustness
Mao, C.; Gupta, A.; Nitin, V.; Ray, B.; Song, S.; Yang, J.; and Vondrick, C. 2020 · 2020
Later among the works it cites.
Which tasks should be learned together in multi-task learning?
Standley, T.; Zamir, A.; Chen, D.; Guibas, L.; Malik, J.; and Savarese, S. 2020 · 2020
Later among the works it cites.
Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Yu, F.; Chen, H.; Wang, X.; Xian, W.; Chen, Y.; Liu, F.; Madhavan, V.; and Darrell, T. 2020 · 2020
Later among the works it cites.
Multi-task learning for dense prediction tasks: A survey
Vandenhende, S.; Georgoulis, S.; Van Gansbeke, W.; Proesmans, M.; Dai, D.; and Van Gool, L. 2021 · 2021
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