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This text summarizes adversarial training for robust models in supervised machine learning. It explains how classifiers are trained using labeled data, such as distinguishing between pandas and pumpkins. Despite working well on natural images, these classifiers fail on adversarial examples. Adversarial examples are when a slight change in an image causes the classifier to misclassify it. The goal is to create adversarial examples that look like the original image but are classified differently. Different metrics can be used to measure the success of this.
