Virtual Adversarial Training (VAT) is a technique used in data augmentation that enhances a model's robustness by generating adversarial examples during training. Essentially, it is focused on creating slightly modified versions of the training data that can trick the model into making incorrect predictions. The goal of VAT is not to generate completely new data but to slightly perturb existing data points in a way that challenges the model, helping it learn to generalize better to unseen data.
The process of VAT works by applying small, imperceptible changes to the input data. For instance, consider an image classification model trained on pictures of cats and dogs. During VAT, the algorithm finds the direction in which modifying the original image most affects the model's predictions. It generates a new, "virtual" example that is close to the original but designed to maximize the prediction error. By training on these adversarial examples alongside the regular data, the model learns to be more resilient against small variations in input—similar to how it would encounter new data in real-world applications.
An additional benefit of VAT is that it doesn’t require additional labeled data, making it a cost-effective solution for training robust models. This is particularly useful in scenarios where labeled data is scarce, such as medical imaging or specialized industrial applications. By continuously refining the model's ability to handle small input variations, VAT helps to produce a model that not only performs well on the training data but also maintains its accuracy when faced with new, unseen examples in practical deployments.