Diffusion models – the pros and cons
In this section, you will learn about and examine the main pros and cons of using DMs for synthetic data generation. This will help you to weigh the advantages and disadvantages of each synthetic data generation method. Consequently, it will give you the wisdom to select the best approach for your own problems.
As we learned in Chapter 7, GANs work very well for certain applications, such as style transfer and image-to-image translation, but they are usually very hard to train and unstable. Additionally, the generated synthetic samples are usually less diverse and photorealistic. Conversely, recent papers have shown that DM-based synthetic data generation approaches surpass GANs on many benchmarks. For more details, please refer to Diffusion Models Beat GANs on Image Synthesis (https://arxiv.org/pdf/2105.05233.pdf). Like any other synthetic data generation approach, DMs have pros and cons. Thus, you need to consider them carefully for...
