end-to-end 究竟是什么意思

看深度学习论文的时候经常看到end-to-end，带着黑人问号看下去也总是没有get到这个end-to-end究竟是什么意思。这里在Quora上找到了一个回答，算是解答了我的疑问吧。

以下是答案的原文

The phrase “end-to-end” doesn’t mean anything particular to deep learning. Much like that VC favorite “from soup to nuts”, it’s just an emphatic adverbial phrase that’s thrown in to convey a vague aura that what you’re doing is somehow more complete, total, and more general than what others are doing.

I repeat, there is no “end-to-end model”. Such a term does not exist. If someone tells you “it’s an end-to-end deep learning model”, they’re trying to communicate completeness without being specific. If you’re reading a paper or blog, then just ignore and look for the details (either in math or in figures). If you’re talking to someone, just ignore and ask questions until the specifics emerge.

Sometimes people do want to emphasize that they are doing less pre-processing or post-processing than others, and then they might invoke a term such as “end-to-end” meaning that they removed some extra processing step that was at common at some point. But it’s never total. There’s always some sort of unpacking and repacking on either end of a real-world system.

总之就是这个end-to-end并不是一个严谨的学术用语。我按照自己的理解来叙述一下。通常我们在做kaggle之类的比赛时，会用到多个模型，比如svm，逻辑回归，随机森林等等，而每一个模型都是单独拿来训练数据的，训练之后我们把多个模型的结果整合到一起。这个过程中，我们还要做很多数据预处理(preprocessing)和数据后处理(postprocessing)的工作。这样的多模型独立训练，并进行很多数据处理的方法我理解为 none end-to-end。而对于神经网络来说，不做过多的数据预处理（只是标准化的话没关系），模型的话也只有一个，预测的结果也是通过这个模型直接输出的，这样一种非常直接的方法我理解为end-to-end。

总有一种行业黑话的感觉，因为不明觉厉所以用得人比较多吧。