End-to-End Model

A modeling paradigm in deep learning in which a single unified neural network maps raw input directly to final output without explicitly decomposing intermediate subtasks. Its advantage is simple and fast training and inference; its drawback is that intermediate processes are invisible and offer limited controllability.