alt Hacker NewsI might be misunderstanding your point but this conflates the distinguishing features of each. you mention expansion but autoencoders canonically compress their inputs. autoencoders have an explicit encoder and decoder. most transformers we interact with these days (LLMs) are decoder only. the manifold isn't typically something the model is applied to directly. we apply the function/model to the latent representations. those are what live on the manifold.
Now that’s interesting.. what exactly distinguishes latent representations and the manifold? IMHO, those are the same, and you’re constructing a piecewise function of the manifold itself. Decoders also produce manifolds much in the same way, with the distinction being that the encoder isn’t learned but static after initialisation. So fundamentally it is still DOING the same operation.