Data Imbalance in Deep Learning: Addressing the Problem and Techniques

Data imbalance is a common problem in deep learning where the number of samples in one class is significantly higher than the other classes. This can lead to biased predictions and poor performance of the model on the minority class. There are several techniques to address this problem, such as data augmentation, class weighting, oversampling, and undersampling.

'Reference:

• He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21(9), 1263-1284.
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
• Zhang, Y., & Sun, G. (2018). Understanding data augmentation for classification: when to warp?. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 1179-1187).'