Using a non-parametric encoder eliminates the need for generalization, as it does not learn an explicit mapping from input to hidden space. Instead, it uses a probabilistic representation that adapts to new inputs without requiring retraining or model updates. However, this approach may lead to increased computational costs and memory requirements due to the need to store and evaluate probability distributions over the entire input space.
