In addition to mapping artificial images to a space of random numbers, we can also use generative models to learn a mapping between one kind of image and a secound.
This kind of model can, for example, be used to convert an image of a horse into that of a zebra(Figure 1.7), create deep fake videos in which one actor's face has been replaced with another's, or transform a photo into a painting(Figures 1.2 and 1.4):
Another fascinating, example of applyingg generative modeling is a study in which lost masterpieces of the artist Pablo Picasso were discovered to have been painted over with another image. After X-ray imaging of The Old Guitarist and The Crouching Beggar indicated that earlier images of a woman and a landscape lay underneath(Figure 1.8), to train a neural style transfer model that transforms black-and-white images (the X-ray radiographs of the overlying paintings) to the coloration of the original artwork. Then, applying this transfer model to the hidden images allowed them to reconstruct colored-in versions of the lost paintings:
All of these models use the previously mentioned GANs, a type of deep learning model proposed in 2014 In addition to changing the contents of an images (such as dogs and humans with similar facial features, as in Figure 1.9), or generate textual descriptions from images(Figure 1.10):
We could also condition the properties of the generated images on some auxiliary information such as labels, an approach used in the GANCogh algorithm, which synthesizes images in the style of different artists by supplying the desired artist as an input to the generative model(Figure 1.4). I will describe these application in Chapter 6, Image Generation with GANs, and Chapter 7, Style Transfer with GANs.
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