Exploring Diffusion Models for Synthetic Data

This chapter introduces you to diffusion models, which are cutting-edge approaches to synthetic data generation. We will highlight the pros and cons of this novel synthetic data generation approach. This will help you to make informed decisions about the best methods to utilize for your own problems. We will highlight the opportunities and challenges of diffusion models. Moreover, this chapter is enriched with a comprehensive practical example, providing hands-on experience in both generating and effectively employing synthetic data for a real-world ML application. As you go through diffusion models, you will learn about the main ethical issues and concerns around utilizing this synthetic data approach in practice. In addition to that, we will review some state-of-the-art research on this topic. Thus, this chapter will equip you with the necessary knowledge to thoroughly understand this novel synthetic data generation approach.

In this...