Yes, data augmentation is indeed useful for small datasets. When a dataset is limited in size, models can struggle to generalize well, leading to overfitting. Overfitting occurs when a model learns the noise and details of the training data instead of the underlying patterns that can help it make accurate predictions. By applying data augmentation techniques, you can artificially increase the size and diversity of your dataset, allowing the model to learn from a broader set of examples and ultimately improve its performance.
For instance, in image classification tasks, data augmentation techniques such as rotation, flipping, scaling, and cropping can be employed. If you have just a few hundred images of cats and dogs, augmenting your dataset by applying these transformations can produce thousands of variations without the need for new images. This not only increases the variance of the training set but also helps the model to learn invariances to these variations, making it more robust when predicting on unseen images.
Moreover, data augmentation can be beneficial in other domains, such as natural language processing. Techniques like synonym replacement, random insertion of words, or back-translation can create variations in text data. For a small dataset of sentences, these augmentations can lead to better training outcomes. By increasing the amount of training data through augmentation, you provide models with more opportunities to learn important features, leading to better performance even when starting with limited data.