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.
Timeline (last 90 days)
An end-to-end model is an autonomous driving architecture that uses a single model to map directly from sensor input to driving decision output, with the intermediate steps completed implicitly by a neural network.
An end-to-end model refers to using a unified neural network to map directly from raw input to final output, without explicitly splitting sub-tasks such as planning, layout, and rendering in between.
End-to-end neural network methods can flexibly learn the hierarchical structure, but it is very difficult to give bounds on the generalization error, making it hard in practice to judge when the model fails.
All Facts (4)
An end-to-end model is an autonomous driving architecture that uses a single model to map directly from sensor input to driving decision output, with the intermediate steps completed implicitly by a neural network.
50%UnverifiedAn end-to-end model refers to using a unified neural network to map directly from raw input to final output, without explicitly splitting sub-tasks such as planning, layout, and rendering in between.
50%UnverifiedEnd-to-end neural network methods can flexibly learn the hierarchical structure, but it is very difficult to give bounds on the generalization error, making it hard in practice to judge when the model fails.
50%Unverified端到端模型的核心优势在于避免模块间信息损失和误差累积，但代价是可解释性低、对训练数据分布敏感、难以应对长程推理任务
50%