What are latent representations?
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.