Given the powerful applications that generative models have, what are the major challenges i n implementing them? As described, most of these models utilize complex data, requiring us to fit large models to capture all the nuances of their features and distribution. This has implications both for the number of examples that we must collect to adequately represent the kind of data we are trying to generate, and the computational resources needed to build the model. We will discuss techniques in Chapter 2, Setting frameworks and graphics processing units (GPIs).
A more subtle problem that comes from having complex data, and the fact that we are trying to generate data rather than a numerical label or value, is that our notion of model accuracy is much more complicated: we cannot simply calculate the distance to a single label or scores.
We will discuss in Chapter 5, Painting Pictures with Neural Networks Using VAEs, and Chapter 6, Image Generation with GANs, how deep generative models such as VAE and GAN algorithms take different approaches to determine whether a generated image is comparable to a real-world image. Finally, as mentioned, our models need to allow us to generate both large and diverse samples, and the various methods we will discuss take different approaches to control the diversity of data.
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