Alleviating Class Imbalance with Data Augmentation

• Data Augmentation falls under a Data-level solution to class imbalance and there are many different strategies for implementation.
• A naive solution to oversampling with Data Augmentation would be a simple random oversampling with small Geometric Transformations such as a 30° rotation
• One problem of oversampling with basic image transformations is that it could cause overfitting on the minority class which is being oversampled
• The biases present in the minority class are more prevalent post-sampling with these techniques.
• Neural Style Transfer is an interesting way to create new images. These new images can be created either through extrapolating style with a foreign style or by interpolating styles amongst instances within the dataset.
• Oversampling with GANs can be done using the entire minority class as ‘real’ examples, or by using subsets of the minority class as inputs to GANs
• The use of evolutionary sampling to find these subsets to input to GANs for class sampling is a promising area for future work.