A compressed, abstract representation space learned by a model where input data is encoded as vectors, capturing meaningful structure and relationships.
A secret map inside the AI's brain where similar things are stored close together — like cats near dogs but far from cars.
An invisible space where AI organizes its understanding — similar concepts are placed near each other, letting the AI see relationships between things.
A compressed, abstract representation space learned by a model where input data is encoded as vectors, capturing meaningful structure and relationships.
The continuous vector space learned by encoder models where data points are represented as dense vectors, with geometric relationships (distance, direction) corresponding to semantic properties.
A learned manifold in ℝⁿ where the data distribution is mapped via an encoder function — traversable for interpolation, disentangled along semantically meaningful axes, and decodable back to observation space, forming the basis of VAEs, diffusion models, and representation learning.
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