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hi this is a summarization for the adversarial training for free paper in which we trained robust models efficiently in supervised machine learning we train models using labeled data lets say we want to build a classifier that can distinguish a panda from a pumpkin we start off with some images of pandas and also some images of pumpkins then usually we have a neural network and we use some optimization routine such as some variant of stochastic gradient descent to build this classifier this classifier works very well on natural images and also images which come from the same distribution as our training data however it is known that these classifiers work horribly on adversarial examples given a classifier F which Maps an image X to the label why we say another example X plus Delta is something which the classifier correctly classifies as something other than Y but we want our adversarial example X plus Delta to look like the clean example X and we can have different measures of this