Comparing Different Automatic Image Augmentation Methods in PyTorch

One of the best ways to reduce overfitting is to collect more (good-quality) data. However, collecting more data is not always feasible or can be very expensive. A related technique is data augmentation. Data augmentation involves generating new data records or features from existing data, expanding the dataset without collecting more data. It helps improve model generalization by creating variations of original input data and making it harder to memorize irrelevant information from training examples or features. Data augmentation is common for image and text data, but also exists for tabular data.