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I am reading some research papers about graph convolutional neural networks and I have seen the term "latent representations" used a lot. For instance, "the model was able to learn latent representations of the nodes of the graph".

What does the word "latent" means here? Is it the same as latent features?

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The latent representation is the simplified model of your input data, for example, created by a neural network.

Considering an autoencoder, the central layer of this network (after training) will contain a simplified representation of the input data (i.e. summary of key features), which can be used to reconstruct the output.

If we take a dictionary definition of Latent: present and capable of emerging or developing but not now visible, obvious, active, or symptomatic, we can see how this describes the somewhat non-existence of the state, rather instead only a latent representation of the input data.

This image is a nice description. The latent representation is key features of the input data (here: the ears, nose, eyes of the animals.)

enter image description here

So yes, the latent representation is the sum of the latent features.

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    $\begingroup$ I think you should reconsider the point i.e. " created by your neural network". It can be without a Neural Network. $\endgroup$
    – 10xAI
    Commented Mar 2, 2021 at 14:24

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