I came across the "reparametrization trick" for the first time in the following paragraph from the chapter named Vector Calculus from the test book titled Mathematics for Machine Learning by Marc Peter Deisenroth et al.
The Jacobian determinant and variable transformations will become relevant ... when we transform random variables and probability distributions. These transformations are extremely relevant in machine learning in the context of training deep neural networks using the reparametrization trick, also called infinite perturbation analysis.
The trick has been used in the context of neural networks training in the quoted paragraph. But when I search about the reparametrization trick, I found it only or widely in training autoencoders.
In the context of training a traditional deep neural network, is the trick useful?