Latent Space in Deep Learning | Baeldung on Computer Science

In this tutorial, we’ll talk about the latent space in deep learning. First, we’ll define the term and discuss its importance to deep learning, and then we’ll present some examples of latent space. Formally, a latent space is defined as an abstract multi-dimensional space that encodes a meaningful internal representation of externally observed events. Samples that are similar in the external world are positioned close to each other in the latent space. To better understand the concept, let’s think about how humans perceive the world. We are able to understand a broad range of topics by encoding each observed event in a compressed representation in our brain. For example, we don’t keep in mind every detail of the appearance of a dog to be able to recognize a dog in the street. As we can see in the illustration below, we keep an internal representation of the general appearance of a dog: In a similar way, the latent space tries to provide a compressed understanding of the world to a comp

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