Book Image

Hands-On Meta Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Meta Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.
Table of Contents (17 chapters)
Title Page
About Packt

Adversarial meta learning

We have seen how MAML is used to find the optimal parameter θ that is generalizable across tasks. Now, we will see a variant of MAML called ADML, which makes use of both clean and adversarial samples to find the better and robust initial model parameter θ. Before going ahead, let's understand what adversarial samples are. Adversarial samples are obtained as a result of adversarial attacks. Let's say we have an image; an adversarial attack consists of slightly modifying this image in such a way that it is not detectable to our eyes, and this modified image is called adversarial image. When we feed this adversarial image to the model, it fails to classify it correctly. There are several different adversarial attacks used to get the adversarial samples. We will see one of the commonly used methods called Fast Gradient Sign Method (FGSM).


Let's say we are performing an image classification; in general, we train the model by computing the loss and trying to minimize...