<ul><li>Data augmentation methods have been proposed for various applications.</li><li>Different approaches, such as human-designed transformations and policy/search-based automated methods, have been used.</li><li>Generic transformations like Gaussian or adversarial noise, dropout, and generative models have also been effective.</li><li>Mixup, a popular data augmentation technique, has been extended for classification tasks but may adversely impact regression predictions.</li></ul>

A Comprehensive Overview of Data Augmentation Methods

Discover more