Improving performance with data augmentation

Data augmentation is a common technique in deep learning to improve the success of a model. It involves replicating a data point by making changes that do not significantly alter its meaning. This technique has been widely used in image processing and Natural Language Processing (NLP) as training the neural model with more data typically leads to better performance.

We can introduce noise or perturbations to the existing data, create new variations or aspects of the same data, or generate entirely new data points through interpolation, where new data is generated based on a couple of existing nearby data points. Data augmentation is especially useful when training data is limited. It can make the model more robust and improve its performance on downstream tasks. There are several ways to perform data augmentation. In image processing, this may include flipping or changing the brightness of images. In NLP, it may include removing or swapping...