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