A challenge to generating images such as the Portraint of Edmond Belamy with the approach used for the MNIST dataset is that frequently, images have no labels (such as a digit); rather, we want to map the space of random numbers into a set of artificial images using a latent vector, Z, as I described earlier in the chapter.
A further constraint is that we want to promote diversity of these images. If we input numbers within a certain range, we would like to know that they generate different outputs, and be able to tune the resulting image features. For this purpose, VAEs were developed to generate diverse and photorealistic image(Figure 1.5).
In the context of image classification tasks, being able to generate new images can help us increase the number of examples in an existing dataset, or reduce the bias if our existing dataset is heavily skewed toward a particular kind of photograph.
Applications could include generating alternative poses(angles, shades, or prespective shots) for product photographs on a fashion e-commerce website(Figure 1.6):
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