Fetching the paper…
Reading the bibliography…
Noisy labels (NL) and adversarial examples both undermine trained models, but interestingly they have hitherto been studied independently.
Where is the bottleneck of adversarial learning with unlabeled data?
Jingfeng Zhang, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama · 1911
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Correcting noisy data
Choh-Man Teng · 1999
Earlier work this paper cites.
Crafting papers on machine learning
P. Langley · 2000
Earlier work this paper cites.
Feature scaling in support vector data descriptions
DM Tax and RP Duin · 2000
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Entropy of function of uncertain variables
Wei Dai and Xiaowei Chen · 2012
Earlier work this paper cites.
Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
Earlier work this paper cites.
Learning with symmetric label noise: The importance of being unhinged
Brendan Van Rooyen, Aditya Menon, and Robert C Williamson · 2015
Earlier work this paper cites.
Deep learning , volume 1
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Earlier work this paper cites.
Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
Cited alongside, same era.
Decoupling” when to update” from” how to update”
Eran Malach and Shai Shalev-Shwartz · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
Later among the works it cites.
How does disagreement help generalization against label corruption?
Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor Tsang, and Masashi Sugiyama · 2019
Later among the works it cites.
Mma training: Direct input space margin maximization through adversarial training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, and Ruitong Huang · 2020
Later among the works it cites.
Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
Later among the works it cites.
What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
Later among the works it cites.
Self-adaptive training: beyond empirical risk minimization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Cited alongside, same era.
Curriculum adversarial training
Qi-Zhi Cai, Chang Liu, and Dawn Song · 2018
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
Cited alongside, same era.
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
Cited alongside, same era.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Lang Huang, Chao Zhang, and Hongyang Zhang · 2020
Later among the works it cites.
Peer loss functions: Learning from noisy labels without knowing noise rates
Yang Liu and Hongyi Guo · 2020
Later among the works it cites.
Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2020
Later among the works it cites.
Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J. Zico Kolter · 2020
Later among the works it cites.
Searching to exploit memorization effect in learning with noisy labels
Quanming Yao, Hansi Yang, Bo Han, Gang Niu, and James Tin-Yau Kwok · 2020
Later among the works it cites.
Attacks which do not kill training make adversarial learning stronger
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, and Mohan Kankanhalli · 2020
Later among the works it cites.
Improving adversarial robustness via channel-wise activation suppressing
Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, and Yisen Wang · 2021
Closest in time.
Robust overfitting may be mitigated by properly learned smoothening
Tianlong Chen, Zhenyu Zhang, Sijia Liu, Shiyu Chang, and Zhangyang Wang · 2021
Closest in time.
Learning with instance-dependent label noise: A sample sieve approach
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu · 2021
Closest in time.
How benign is benign overfitting?
Amartya Sanyal, Puneet K Dokania, Varun Kanade, and Philip H. S. Torr · 2021
Closest in time.
Geometry-aware instance-reweighted adversarial training
Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, and Mohan Kankanhalli · 2021
Closest in time.