Generative AI Tutorial – Slide 55
Understanding Latent Space Representations in Generative Models
Understanding Latent Space Representations in Generative Models
Slide 55 introduces the concept of *latent space*—a compressed mathematical representation used by generative AI models to encode meaning, structure, and features of data. This hidden representation enables models to generate new outputs, interpolate between concepts, and understand relationships that are not explicitly labeled.
A compressed vector representation containing meaningful patterns the AI has learned.
Numerical vectors that represent items (text, images, audio) in latent space.
Moving between two latent vectors to generate blended or intermediate outputs.
Images, text, audio, etc.
Model compresses complex data.
Dense, meaningful representation.
Reconstructs or generates new outputs.
A numerical representation capturing the essential qualities of the input.
It simplifies complex data so the model can more easily generate variations.
Sometimes—individual dimensions can correlate with meaningful attributes.
Explore the next slides to deepen your understanding of how models generate rich, meaningful outputs.
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