Fetching the paper…
Reading the bibliography…
Classifiers in machine learning are often brittle when deployed.
Tangent prop - a formalism for specifying selected invariances in an adaptive network
P. Y. Simard, B. Victorri, Y. LeCun, and J. S. Denker · 1991
Earlier work this paper cites.
Document image defect models
H. S. Baird · 1992
Earlier work this paper cites.
Efficient pattern recognition using a new transformation distance
P. Y. Simard, Y. LeCun, and J. S. Denker · 1992
Earlier work this paper cites.
Effective training of a neural network character classifier for word recognition
L. S. Yaeger, R. F. Lyon, and B. J. Webb · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Transformation invariance in pattern recognition - tangent distance and tangent propagation
P. Y. Simard, Y. LeCun, J. S. Denker, and B. Victorri · 1998
Earlier work this paper cites.
Continuous probabilistic transform for voice conversion
Y. Stylianou, O. Cappé, and E. Moulines · 1998
Earlier work this paper cites.
Best practices for convolutional neural networks applied to visual document analysis
P. Y. Simard, D. Steinkraus, and J. C. Platt · 2003
Earlier work this paper cites.
Semi-supervised semantic role labeling using the latent words language model
K. Deschacht and M.-F. Moens · 2009
Earlier work this paper cites.
Model-portability experiments for textual temporal analysis
O. Kolomiyets, S. Bethard, and M.-F. Moens · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Vocal tract length perturbation (vtlp) improves speech recognition
N. Jaitly and E. S. Hinton · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Learning to disentangle factors of variation with manifold interaction
S. E. Reed, K. Sohn, Y. Zhang, and H. Lee · 2014
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Earlier work this paper cites.
Data augmentation for deep neural network acoustic modeling
X. Cui, V. Goel, and B. Kingsbury · 2015
Earlier work this paper cites.
Deep manifold traversal: Changing labels with convolutional features
J. R. Gardner, M. J. Kusner, Y. Li, P. Upchurch, K. Q. Weinberger, and J. E. Hopcroft · 2015
Earlier work this paper cites.
Audio augmentation for speech recognition
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. D. McDaniel, X. Wu, S. Jha, and A. Swami · 2015
Earlier work this paper cites.
Deep visual analogy-making
S. E. Reed, Y. Zhang, Y. Zhang, and H. Lee · 2015
Earlier work this paper cites.
Improving neural machine translation models with monolingual data
R. Sennrich, B. Haddow, and A. Birch · 2015
Earlier work this paper cites.
That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using petpeeve tweets
W. Y. Wang and D. Yang · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
X. Zhang, J. J. Zhao, and Y. LeCun · 2015
Earlier work this paper cites.
Neural photo editing with introspective adversarial networks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Data recombination for neural semantic parsing
R. Jia and P. Liang · 2016
Earlier work this paper cites.
Conditional image synthesis with auxiliary classifier gans
A. Odena, C. Olah, and J. Shlens · 2016
Earlier work this paper cites.
Improving the robustness of deep neural networks via stability training
S. Zheng, Y. Song, T. Leung, and I. Goodfellow · 2016
Earlier work this paper cites.
Data augmentation generative adversarial networks
A. Antoniou, A. Storkey, and H. Edwards · 2017
Earlier work this paper cites.
Adversarial transformation networks: Learning to generate adversarial examples
S. Baluja and I. C. Fischer · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
Cited alongside, same era.
Cut, paste and learn: Surprisingly easy synthesis for instance detection
D. Dwibedi, I. Misra, and M. Hebert · 2017
Cited alongside, same era.
A rotation and a translation suffice: Fooling cnns with simple transformations
L. Engstrom, D. Tsipras, L. Schmidt, and A. Madry · 2017
Cited alongside, same era.
Data augmentation for low-resource neural machine translation
M. Fadaee, A. Bisazza, and C. Monz · 2017
Cited alongside, same era.
Robustness via curvature regularization, and vice versa
S.-M. Moosavi-Dezfooli, A. Fawzi, J. Uesato, and P. Frossard · 2018
Later among the works it cites.
Constructing unrestricted adversarial examples with generative models
Y. Song, R. Shu, N. Kushman, and S. Ermon · 2018
Later among the works it cites.
Transferring gans: generating images from limited data
Y. Wang, C. Wu, L. Herranz, J. van de Weijer, A. Gonzalez-Garcia, and B. Raducanu · 2018
Later among the works it cites.
Generating adversarial examples with adversarial networks
C. Xiao, B. Li, J.-Y. Zhu, W. He, M. Liu, and D. X. Song · 2018
Later among the works it cites.
Qanet: Combining local convolution with global self-attention for reading comprehension
A. W. Yu, D. Dohan, M.-T. Luong, R. Zhao, K. Chen, M. Norouzi, and Q. V. Le · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Conditional variance penalties and domain shift robustness
C. Heinze-Deml and N. Meinshausen · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
Cited alongside, same era.
Geometric robustness of deep networks: Analysis and improvement
C. Kanbak, S.-M. Moosavi-Dezfooli, and P. Frossard · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Cited alongside, same era.
Learning to compose domain-specific transformations for data augmentation
A. J. Ratner, H. Ehrenberg, Z. Hussain, J. Dunnmon, and C. Ré · 2017
Cited alongside, same era.
Learning to compose domain-specific transformations for data augmentation
A. J. Ratner, H. R. Ehrenberg, Z. Hussain, J. Dunnmon, and C. Ré · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Cited alongside, same era.
X. Zhang, Z. Wang, D. Liu, and Q. Ling · 2018
Later among the works it cites.
M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019
Later among the works it cites.
Synthetic examples improve generalization for rare classes
S. Beery, Y. Liu, D. Morris, J. Piavis, A. Kapoor, M. Meister, and P. Perona · 2019
Later among the works it cites.
Mixmatch: A holistic approach to semi-supervised learning
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel · 2019
Later among the works it cites.
(de) constructing bias on skin lesion datasets
A. Bissoto, M. Fornaciali, E. Valle, and S. Avila · 2019
Later among the works it cites.
Unlabeled samples generated by gan improve the person re-identification baseline
W. chen Sun, F. Liu, and W. Xu · 2019
Later among the works it cites.
Autoaugment: Learning augmentation strategies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
Later among the works it cites.
Randaugment: Practical data augmentation with no separate search
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le · 2019
Later among the works it cites.
Achieving robustness in the wild via adversarial mixing with disentangled representations
S. Gowal, C. Qin, P.-S. Huang, T. Cemgil, K. Dvijotham, T. Mann, and P. Kohli · 2019
Later among the works it cites.
Augmix: A simple data processing method to improve robustness and uncertainty
D. Hendrycks, N. Mu, E. D. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan · 2019
Later among the works it cites.
Population based augmentation: Efficient learning of augmentation policy schedules
D. Ho, E. Liang, I. Stoica, P. Abbeel, and X. Chen · 2019
Later among the works it cites.
Learning data manipulation for augmentation and weighting
Z. Hu, B. Tan, R. Salakhutdinov, T. M. Mitchell, and E. P. Xing · 2019
Later among the works it cites.
Fast autoaugment
S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim · 2019
Later among the works it cites.
Art, creativity, and the potential of artificial intelligence
M. Mazzone and A. Elgammal · 2019
Later among the works it cites.
Adversarial learning of general transformations for data augmentation
S. Mounsaveng, D. Vázquez, I. B. Ayed, and M. Pedersoli · 2019
Later among the works it cites.
Mnist-c: A robustness benchmark for computer vision
N. Mu and J. Gilmer · 2019
Later among the works it cites.
Adaptive augmentation of medical data using independently conditional variational auto-encoders
M. Pesteie, P. Abolmaesumi, and R. Rohling · 2019
Later among the works it cites.
Semanticadv: Generating adversarial examples via attribute-conditional image editing
H. Qiu, C. Xiao, L. Yang, X. Yan, H. Lee, and B. Li · 2019
Later among the works it cites.
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
L. Rieger, C. Singh, W. J. Murdoch, and B. Yu · 2019
Later among the works it cites.
S. Sagawa, P. W. Koh, T. B. Hashimoto, and P. Liang · 2019
Later among the works it cites.
Data augmentation using generative adversarial networks (cyclegan) to improve generalizability in ct segmentation tasks
V. Sandfort, K. Yan, P. J. Pickhardt, and R. M. Summers · 2019
Later among the works it cites.
Eda: Easy data augmentation techniques for boosting performance on text classification tasks
J. Wei and K. Zou · 2019
Later among the works it cites.
Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition
J. K. Winkler, C. Fink, F. Toberer, A. Enk, T. Deinlein, R. Hofmann-Wellenhof, L. Thomas, A. Lallas, A. Blum, W. Stolz, et al · 2019
Later among the works it cites.
Unsupervised data augmentation for consistency training
Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q. V. Le · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo · 2019
Later among the works it cites.
Learning data augmentation strategies for object detection
B. Zoph, E. D. Cubuk, G. Ghiasi, T.-Y. Lin, J. Shlens, and Q. V. Le · 2019
Later among the works it cites.
Stargan v2: Diverse image synthesis for multiple domains
Y. Choi, Y. Uh, J. Yoo, and J.-W. Ha · 2020
Closest in time.