Style Transfer and Image Transformation
Explore how generative AI models map and transform images by learning complex mappings, enabling creations like horse-to-zebra conversions, deepfake videos, and colorized lost artworks. Understand the principles behind neural style transfer, GANs, and their application to images, text, sound, and game rules, along with the challenges faced when generating diverse and realistic synthetic data.
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Generative models can be used to map artificial images to a space of random numbers; they can also learn the mapping between one kind of image and another. This kind of model can, for example, be used to convert an image of a horse into that of a
Another fascinating example of applying 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 indicated that earlier images of a woman and a landscape lay underneath, researchers used the other paintings from Picasso’s blue period or other color photographs to train a neural style transfer model that transforms black-and-white images (the X-ray radiographs of the overlying painting) to the coloration of the original
In the figure below, deep learning was used to color in the X-ray images of the painted-over scenes (c), with ...