Deep papers: A survey on Image Data Augmentation for Deep Learning part 1
data augmentation comes in handy when we have a small number of training samples or unbalanced datasets. in this survey, Connor Shorten and Taghi M. Khoshgoftaar discussed data augmentation techniques and how they affect model performance. we are going to test those technics with a small dataset with two classes slightly unbalanced. authors mainly break down augmentation techniques into two branches, which are basic image manipulations and deep learning approaches. in the first part, we only discuss basic image manipulations. those techniques are also known as geometric transformations. Why augmentations? maybe you think why augmentation, we can try to gather more training samples. that's not the optimal solution always. sometimes it's. but when gathering more training samples will be more expensive, maybe there is some level of rarity issues that prevent gathering more training samples and also there may be privacy issues, maybe health issues like CT scanning likewise, gatheri
