Adversarial Training

Adversarial training is a technique used in machine learning to improve the robustness of a model against adversarial attacks. Adversarial attacks are maliciously crafted inputs designed to cause a model to misclassify or produce incorrect outputs. Adversarial training involves generating adversarial examples from the training data and adding them to the training set to improve the model's ability to handle such attacks. The process involves training the model on both the original and adversarial examples, which helps the model learn to distinguish between the two. This technique has been shown to be effective in improving the accuracy and robustness of models against adversarial attacks.
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