Aug 7 in General discussion
Understanding the Complexity of GenAI Models: Navigating Multiple Categories and Applications
It’s easy to get confused when GenAI models are complex and can fit into multiple categories at once.
In practice, many tools don’t belong to just one category. For example, GPT-4 is a transformer-based model, a multimodal model, and a large language model (LLM) all at once.
*Popular Generative Models (simplified):
GANs (Generative Adversarial Networks): Two neural networks compete, improving content generation over time.
Transformer-based models: Learn from long-range dependencies between words.
Diffusion Models: Generate complex data by adding and refining noise iteratively.
VAEs (Variational Autoencoders): Encode data into a compact form, enabling creative content generation through latent space interpolation.
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Understanding the Complexity of GenAI Models: Navigating Multiple Categories and Applications
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