J. Cho, K. Lee, E. Shin, G. Choy, and S. Do, "How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?, " arXiv preprint arXiv:1511.06348. 2015.
C. N. Vasconcelos and B. N. Vasconcelos, "Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation, " arXiv preprint arXiv:1702.07025. 2017.
T. Shaikhina and N. A. Khovanova, "Handling limited datasets with neural networks in medical applications: A small-data approach, " Artif. Intell. Med., vol. 75, pp. 51-63, 2017.
C. Shorten and T. M. Khoshgoftaar, "A survey on Image Data Augmentation for Deep Learning, " J. Big Data, vol. 6, no. 1, p. 60, 2019.
L. Perez and J. Wang, "The Effectiveness of Data Augmentation in Image Classification using Deep Learning, " arXiv preprint arXiv:1712.04621. 2017.
J. Kukacka, V. Golkov, and D. Cremers, "Regularization for Deep Learning: A Taxonomy, " arXiv preprint arXiv:1710.10686. 2017.
Krizhevsky, Alex, and G. Hinton, "Learning Multiple Layers of Features from Tiny Images, " 2009.
E. Zawadzka-Gosk, K. Wolk, and W. Czarnowski, "Deep Learning in State-of-the-Art Image Classification Exceeding 99% Accuracy, " in World Conference on Information Systems and Technologies, 2019, pp. 946-957.
B. Wei, Z. Han, X. He, and Y. Yin, "Deep learning model based breast cancer histopathological image classification, " in 2017 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017, 2017, pp. 348-353.
H. H. Alam, M. M. Rahoman and M. K. A. Azad, "Sentiment Analysis for Bangla Sentences using Convolutional Neural Network", in 2017 International Conference of Computer and Information Technology, ICCIT 2017, 2017.
A. Antoniou, A. Storkey, and H. Edwards, "Data Augmentation Generative Adversarial Networks, " arXiv preprint arXiv:1711.04340. 2017.
H. Mengxiao and J. Li, "Exploring Bias in GAN-based Data Augmentation for Small Samples, " arXiv preprint arXiv:1905.08495. 2019.
C. Lei, B. Hu, D. Wang, S. Zhang, and Z. Chen, "A preliminary study on data augmentation of deep learning for image classification, " in Proceedings of the 11th Asia-Pacific Symposium on Internetware, 2019, pp. 1-6.
S. O'Gara and K. McGuinness, "Comparing Data Augmentation Strategies for Deep Image Classification, " in Irish Machine Vision and Image Processing Conference (IMVIP), 2019.
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, "AutoAugment: Learning augmentation strategies from data, " in Proceedings of the IEEE conference on computer vision and pattern recognition, 2019, pp. 113-123.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting, " J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, 2014.
J. Ba and B. Frey, "Adaptive dropout for training deep neural networks, " in Advances in Neural Information Processing Systems, 2013, pp. 3084-3092.
H. Wu and X. Gu, "Towards dropout training for convolutional neural networks, " Neural Networks, vol. 71, pp. 1-10, 2015.
S. Park and N. Kwak, "Analysis on the Dropout Effect in Convolutional Neural Networks, " ACCV 2016 Comput. Vis.-ACCV 2016 pp, pp. 189-204, 2016.
S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift, " in 32nd International Conference on Machine Learning, ICML 2015, 2015, pp. 448-456.
I. Gitman and B. Ginsburg, "Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification, " arXiv preprint arXiv:1709.08145. 2017.
R. Roelofs, "Measuring Generalization and Overfitting in Machine Learning, " Doctoral dissertation, UC Berkeley. 2019.
B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro, "Exploring generalization in deep learning, " Adv. Neural Inf. Process. Syst., pp. 5947-5956, 2017.
M. J. Anzanello and F. S. Fogliatto, "Learning curve models and applications: Literature review and research directions, " Int. J. Ind. Ergon., vol. 41, no. 5, pp. 573-583, 2011.
F. Pinel et al., "Evolving a deep neural network training time estimator, " in Proceedings of the 2020 Int. Conference on Optimization and Learning (OLA'20), 2020, pp. 13-24.