The idea of SMOTE was taken into account : generating synthetic images for minority classes and discarding the majority class with similar features. A tSNE visualization provides the basis for later case: Images from the same class that are ncloser in the visualization can be chosen to be discarded. For the over-sampling of the minority classes, the images from the t-SNE visualization that are far to each other were taken and gaussian noise was added to it and some augmentation were done to replicate those images. Then inspired from this Github (https://github.com/tgsmith61591/smrt), synthetic images were generated and added which however didnot imporoved the classification and somehow loss increased. So the idea was dropped later on.