Adversarial training is a type of machine learning technique used in deep learning. It is a form of supervised learning in which a model is trained on labeled data and used to predict the output of a given input. Unlike other forms of supervised learning, adversarial training incorporates an adversarial element which encourages improved accuracy and robustness of the trained model.
Adversarial training works by introducing a small number of false examples to the training dataset. These false examples are created by “adversarial” methods which modify a true example in a way that would cause it to be misclassified. The goal of adversarial training is to create a model that can accurately classify both true and false examples.
Adversarial training is particularly useful in image classification tasks, where it can increase accuracy by up to 15%. In addition to image classification, it is also used in speech recognition, natural language processing, and many other fields.
Adversarial training has become increasingly popular due to its potential for better generalization, robustness, and accuracy. It is also important to note that adversarial training requires an immense amount of computing power and data since a large number of false examples must be created in order to train the model.
