Hands-On Meta Learning with Python

ADML

Now that we have seen what adversarial samples are and how to generate adversarial samples, we will see how to use these adversarial samples in meta learning. We train our meta learning model with both clean samples and adversarial samples. But what is the need for training the model with adversarial samples? It helps us to find the robust model parameter θ. Both the clean and adversarial samples are used in the inner and outer loops of the algorithm and contribute equally to update the model parameter. ADML uses this varying correlation between clean and adversarial samples to obtain a better and robust initialization of model parameters so that our parameter becomes robust to adversarial samples and generalizes well to new tasks.