References
- R. Caruana, S. Lawrence, and C.L. Giles, “Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping”, Advances in Neural Information Processing Systems, vol. 13, pp. 402–408.
- C. Shorten and T.M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning”, Journal of Big Data, vol. 6, no. 1, pp. 1–48 doi: 10.1186/s40537-019-0197-0
- J. Wang and L. Perez, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning”, arXiv preprint arXiv:1712.04621, 2017.
- A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images”, Technical Report, University of Toronto, 2009.
- S.G. Müller and F. Hutter, “TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 774–782.
- E.D. Cubuk “AutoAugment: Learning Augmentation Strategies from Data”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 113–123.
- Bischl Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 132, e1484 https://doi.org/10.1002/widm.1484
- A. Krizhevsky, I. Sutskever, and G.E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Communications of the ACM, vol. 60, no. 6, pp. 84–90.
- P.Y. Simard, D. Steinkraus, and J.C. Platt, “Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis”, Proceedings of the Seventh International Conference on Document Analysis and Recognition, vol. 2, pp. 958–963.
- K. Chatfield “Return of the Devil in the Details: Delving Deep into Convolutional Nets”, British Machine Vision Conference, 2014.
- S.G. Müller, L. Hollmann, and F. Hutter, “Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 13, no. 2, e1484, 2023.
- L. Taylor and G. Nitschke, “Improving Deep Learning with Generic Data Augmentation”, IEEE Symposium Series on Computational Intelligence, pp. 1–8.
- D. Hendrycks “AugMax: Adversarial Composition of Random Augmentations for Robust Training”, Advances in Neural Information Processing Systems, vol. 34, pp. 237–249.
- H. Zhang, “Mixup: Beyond Empirical Risk Minimization”, International Conference on Learning Representations, 2018.
- S. Yun, “CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032.
- F. Zhao, J. Huang, and J. Gao, “Data Augmentation for Deep Learning-Based Radio Modulation Classification”, IEEE Access, vol. 7, pp. 15713–15722.
- J. Lemley, S. Bazrafkan, and P. Corcoran, “Smart Augmentation Learning an Optimal Data Augmentation Strategy”, IEEE Access, vol. 5, pp. 5858–5869 doi: 10.1109/ACCESS. 2017.2696121
