A dataset is imbalanced if the classification labels are not equally represented, hence imbalance on the order of 100 to 1 is a common problem in a large number of a real-world scenario such as fraud detection. There have been a large number of attempts to tackle the issue. However, this problem is still widely discussed and an active area of research. This is a Pytorch Implementation of SMOTE.
SMOTE: Synthetic Minority Over-sampling Technique: https://arxiv.org/pdf/1106.1813.pdf
