Deep learning models, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are the backbone of many generative AI systems. GANs consist of two neural networks—a generator and a discriminator—working together. The generator creates content, while the discriminator evaluates its quality. Through iterative training, the generator improves its ability to create realistic outputs. VAEs, on the other hand, are designed to learn an efficient representation of data, which helps them generate new data similar to the input dataset. These models allow generative AI to create everything from realistic images and videos to music and text, by learning the complex patterns within large datasets and producing novel outputs.
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