Fetching the paper…
Reading the bibliography…
While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real-world settings.
Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
Earlier work this paper cites.
Noise or Signal: The Role of Image Backgrounds in Object Recognition
Xiao, K.; Engstrom, L.; Ilyas, A.; and Madry, A. 2020 · 2006
Earlier work this paper cites.
Learning perturbation sets for robust machine learning
Wong, E.; and Kolter, J. Z. 2020 · 2007
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. 2009 · 2009
Earlier work this paper cites.
Transformation properties of learned visual representations
Cohen, T. S.; and Welling, M. 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mirza, M.; and Osindero, S. 2014 · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2015 · 2015
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Isola, P.; Zhu, J.-Y.; Zhou, T.; and Efros, A. A. 2017 · 2017
Earlier work this paper cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J.; Hariharan, B.; van der Maaten, L.; Fei-Fei, L.; Lawrence Zitnick, C.; and Girshick, R. 2017 · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
Cited alongside, same era.
Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H.; Xu, T.; Li, H.; Zhang, S.; Wang, X.; Huang, X.; and Metaxas, D. N. 2017 · 2017
Cited alongside, same era.
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J.; Wang, T.; Yatskar, M.; Ordonez, V.; and Chang, K.-W. 2017 · 2017
Cited alongside, same era.
Blender - a 3D modelling and rendering package
Blender Online Community, A. 2018 · 2018
Cited alongside, same era.
Unsupervised Representation Learning by Predicting Image Rotations
Gidaris, S.; Singh, P.; and Komodakis, N. 2018 · 2018
Cited alongside, same era.
Do cifar-10 classifiers generalize to cifar-10?
Recht, B.; Roelofs, R.; Schmidt, L.; and Shankar, V. 2018 · 2018
Later among the works it cites.
Certifying Some Distributional Robustness with Principled Adversarial Training
Sinha, A.; Namkoong, H.; and Duchi, J. 2018 · 2018
Later among the works it cites.
Generalizing to unseen domains via adversarial data augmentation
Volpi, R.; Namkoong, H.; Sener, O.; Duchi, J. C.; Murino, V.; and Savarese, S. 2018 · 2018
Later among the works it cites.
Group normalization
Wu, Y.; and He, K. 2018 · 2018
Later among the works it cites.
Attgan: Facial attribute editing by only changing what you want
He, Z.; Zuo, W.; Kan, M.; Shan, S.; and Chen, X. 2019 · 2019
Later among the works it cites.
Semantic adversarial attacks: Parametric transformations that fool deep classifiers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Women also snowboard: Overcoming bias in captioning models
Hendricks, L. A.; Burns, K.; Saenko, K.; Darrell, T.; and Rohrbach, A. 2018 · 2018
Cited alongside, same era.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Hendrycks, D.; and Dietterich, T. 2018 · 2018
Cited alongside, same era.
Kannan, H.; Kurakin, A.; and Goodfellow, I. 2018 · 2018
Cited alongside, same era.
Beyond pixel norm-balls: Parametric adversaries using an analytically differentiable renderer
Liu, H.-T. D.; Tao, M.; Li, C.-L.; Nowrouzezahrai, D.; and Jacobson, A. 2018 · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
Cited alongside, same era.
Certified Defenses against Adversarial Examples
Raghunathan, A.; Steinhardt, J.; and Liang, P. 2018 · 2018
Cited alongside, same era.
Joshi, A.; Mukherjee, A.; Sarkar, S.; and Hegde, C. 2019 · 2019
Later among the works it cites.
A style-based generator architecture for generative adversarial networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
Later among the works it cites.
Anomalous Example Detection in Deep Learning: A Survey
Bulusu, S.; Kailkhura, B.; Li, B.; Varshney, P. K.; and Song, D. 2020 · 2020
Closest in time.
Learning to learn single domain generalization
Qiao, F.; Zhao, L.; and Peng, X. 2020 · 2020
Closest in time.
Test-time training with self-supervision for generalization under distribution shifts
Sun, Y.; Wang, X.; Liu, Z.; Miller, J.; Efros, A. A.; and Hardt, M. 2020 · 2020
Closest in time.
Spatial transformer networks
Jaderberg, M.; Simonyan, K.; Zisserman, A.; et al. 2015 · 2025
Closest in time.