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Summarizing Most Popular Text-to-Image Synthesis Methods With Python

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Embedding as illustrated in the below diagram is passed through the Conditioning Augmentation block (a single linear layer) to obtain the textual part of the latent vector (uses VAE like parameterization technique) for the GAN as input. The second part of the latent vector is random Gaussian noise. The latent vector yielded is then fed to the generator part of the GAN. The embedding thus formed is finally fed to the final layer of the discriminator for conditional distribution matching.


An imaginative bot that draws a picture from a dozen words

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If you were asked to draw a picture of several people in ski gear, standing in the snow, chances are you'd start with an outline of three or four people reasonably positioned in the center of the canvas, then sketch in the skis under their feet. Though it was not specified, you might decide to add a backpack to each of the skiers to jibe with expectations of what skiers would be sporting. Finally, you'd carefully fill in the details, perhaps painting their clothes blue, scarves pink, all against a white background, rendering these people more realistic and ensuring that their surroundings match the description. Finally, to make the scene more vivid, you might even sketch in some brown stones protruding through the snow to suggest that these skiers are in the mountains. Now there's a bot that can do all that.


An imaginative bot that draws a picture from a dozen words

#artificialintelligence

If you were asked to draw a picture of several people in ski gear, standing in the snow, chances are you'd start with an outline of three or four people reasonably positioned in the center of the canvas, then sketch in the skis under their feet. Though it was not specified, you might decide to add a backpack to each of the skiers to jibe with expectations of what skiers would be sporting. Finally, you'd carefully fill in the details, perhaps painting their clothes blue, scarves pink, all against a white background, rendering these people more realistic and ensuring that their surroundings match the description. Finally, to make the scene more vivid, you might even sketch in some brown stones protruding through the snow to suggest that these skiers are in the mountains. Now there's a bot that can do all that.


Summarizing Most Popular Text-to-Image Synthesis methods with Python

#artificialintelligence

Automatic synthesis of realistic images from text has become popular with deep convolutional and recurrent neural network architectures to aid in learning discriminative text feature representations. Discriminative power and strong generalization properties of attribute representations even though attractive, its a complex process and requires domain-specific knowledge. Over the years the techniques have evolved as auto-adversarial networks in space of machine learning algorithms continue to evolve. In comparison, natural language offers an easy, general, and flexible plugin that can be used to identify and describing objects across multiple domains by means of visual categories. The best thing is to combine the generality of text descriptions with the discriminative power of attributes.


Generative adversarial networks: What GANs are and how they've evolved

#artificialintelligence

Perhaps you've read about AI capable of producing humanlike speech or generating images of people that are difficult to distinguish from real-life photographs. More often than not, these systems build upon generative adversarial networks (GANs), which are two-part AI models consisting of a generator that creates samples and a discriminator that attempts to differentiate between the generated samples and real-world samples. This unique arrangement enables GANs to achieve impressive feats of media synthesis, from composing melodies and swapping sheep for giraffes to hallucinating footage of ice skaters and soccer players. In point of fact, it's because of this prowess that GANs have been used to produce problematic content like deepfakes, which is media that takes a person in existing media and replaces them with someone else's likeness. The evolution of GANs -- which Facebook AI research director Yann LeCun has called the most interesting idea of the decade -- is somewhat long and winding, and very much continues to this day.