Fetching the paper…
Reading the bibliography…
It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on.
B. Zoph, E. D. Cubuk, G. Ghiasi, T. Lin, J. Shlens, Q. V. Le, Learning data augmentation strategies for object detection (2019) · 1906
Earlier work this paper cites.
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition (2014) · 1906
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research 15 (56) (2014) 1929–1958
1958
Earlier work this paper cites.
H. S. Baird, Document image defect models, in: Structured Document Image Analysis, 1992, pp. 546–556
1992
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11) (1998) 2278–2324
1998
Earlier work this paper cites.
P. Y. Simard, D. Steinkraus, J. C. Platt, Best practices for convolutional neural networks applied to visual document analysis, in: Seventh International Conference on Document Analysis and Recognition, 2003, pp. 958–963
2003
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, F.-F. Li, Imagenet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition (2009) 248–255
2009
Earlier work this paper cites.
V. Nair, G. Hinton, Rectified linear units improve restricted boltzmann machines vinod nair, in: Proceedings of ICML, Vol. 27, 2010, pp. 807–814
2010
Earlier work this paper cites.
A. Krizhevsky, Learning multiple layers of features from tiny images (2012)
2012
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems 25, 2012, pp. 1097–1105
2012
Cited alongside, same era.
T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollár, C. L. Zitnick, Microsoft COCO: common objects in context (2014) · 2014
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Advances in Neural Information Processing Systems 27, 2014, pp. 2672–2680
2014
Cited alongside, same era.
D. Kingma, J. Ba, Adam: A method for stochastic optimization (2015) · 2015
Later among the works it cites.
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, Photo-realistic single image super-resolution using a generative adversarial network (2016) · 2016
Later among the works it cites.
L. Perez, J. Wang, The effectiveness of data augmentation in image classification using deep learning (2017) · 2017
Later among the works it cites.
X. Zhu, Y. Liu, Z. Qin, J. Li, Data augmentation in emotion classification using generative adversarial networks (2017) · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition (2015) · 2015
Cited alongside, same era.
S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015) · 2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015) · 2015
Cited alongside, same era.
Y. Le, X. Yang, Tiny imagenet visual recognition challenge (2015). URL https://tiny-imagenet.herokuapp.com/
2015
Cited alongside, same era.
A. J. Ratner, H. R. Ehrenberg, Z. Hussain, J. Dunnmon, C. Ré, Learning to compose domain-specific transformations for data augmentation (2017) · 2017
Later among the works it cites.
G. Cheng, J. Han, X. Lu, Remote sensing image scene classification: Benchmark and state of the art, Proceedings of the IEEE 105 (10) (2017) 1865–1883
2017
Later among the works it cites.
A. Paszke, et al., Pytorch: An imperative style, high-performance deep learning library, in: Advances in Neural Information Processing Systems 32, 2019, pp. 8024–8035
2019
Later among the works it cites.