Introduction to semi-supervised learning and adversarial training

So how can we improve the model? One approach is to continue to train our model on our image set but during the training we will generate adversarial noise that we add to the image. Since we're training our model, we still know all the labels of our images and we can train the model to classify the images according to the specific label even when the image contains particular noise. This method of'adversarial training' helps generalize the model and makes it more robust against noise that the images might include. It therefore makes the model less likely to make wrong predictions when images outside the training set contain perturbations.