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Types of Generative AI
Generative AI comes in a variety of forms, each with unique advantages and uses. Some of the most typical
varieties are as follows:
• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Recurrent Neural Networks (RNNs)
• Autoencoders (AEs)
Let's study about them in detail.
Generative Adversarial Networks (GANs)
GANs are neural networks that work to produce fresh data. It is made up of two neural networks, which work
together in a unique adversarial process to create realistic synthetic data. These two neural networks are as
follows:
• Generator Network: It produces data that is as close as possible to real data.
• Discriminator Network: It analyses data and provides feedback, i.e. it takes real data and the data generated
by the generator as input and attempts to distinguish between the two.
These two networks work together in a cycle where the generator tries to create realistic fake data, and the
discriminator tries to identify whether the data is real or fake. This back-and-forth process helps the generator
improve and produce more convincing data over time.
Generator Discriminator
network: network:
produces the analyses the
data data and shares
feedback
Some of the examples of GANs are as follows:
• It can create portraits of non-existing people.
• It can convert images from day to night.
• It can generate images based on textual description, for example, if we give a description of a bird, then it will
create an image that is similar to the description.
• It can generate realistic video, which can be used in film production, video games, and generating synthetic data
for training other AI models, etc.
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