Generative AI
Learn about the basics of advanced AI architectures and their real-world applications.
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While convolutional neural networks (CNNs), their derivatives, and other deep learning architectures resulted in immense progress in AI-based applications, in the last decade, machine learning researchers and engineers started exploring models that could learn representations from data to generate new content. This evolution resulted in the rise of generative models such as variational autoencoders and generative adversarial networks.
Encoder-decoder architectures
These encoder-decoder architectures, such as variational autoencoders (VAEs), became a popular choice in applying probabilistic inference to encode the data into a representational latent space while imposing reasonable constraints for smooth interpolation and manipulations. In contrast, generative adversarial networks (GANs) consist of two competing neural networks—a generator that can create synthetic samples of the data and a discriminator block that evaluates whether the generated content is real or fake.
These theoretical ideas resulted in several high-impact products; for example, ...