Ablation Study 消融实验

The term “ablation study” is often use …

2021-08-29

The term “ablation study” is often used in the context of neural networks, especially relatiavely complex ones such as R-CNNs. The idea is to learn about the network by removing parts of it and studying it’s performance.            —-– Robert Long

        术语“消融研究”通常用于神经网络的语境中,尤其是相对复杂的神经网络,例如 R-CNN。 这个想法是通过移除部分网络并研究其性能来了解网络。

         “消融”的本义是手术切除身体组织。 “消融研究”一词起源于 1960 年代和 1970 年代的实验神经心理学领域,当时动物大脑的一部分被移除以研究这对其行为的影响。

        在机器学习的背景下,尤其是复杂的深度神经网络中,“消融研究”被用来描述去除网络某些部分的过程,以便更好地了解网络的行为。

         自从 Keras 深度学习框架的主要作者 Francois Chollet 在 2018 年 6 月发布一条推文以来,该术语就受到了关注:

Ablation studies are crucial for deep learning research — can’t stress this enough. Understanding causality in your system is the most straightforward way to generate reliable knowledge (the goal of any research). And ablation is a very low-effort way to look into causality.

If you take any complicated deep learning experimental setup, chances are you can remove a few modules (or replace some trained features with random ones) with no loss of performance. Get rid of the noise in the research process: do ablation studies.

Can’t fully understand your system? Many moving parts? Want to make sure the reason it’s working is really related to your hypothesis? Try removing stuff. Spend at least ~10% of your experimentation time on an honest effort to disprove your thesis.

        简而言之就是:当你要验证一个系统当中不同的功能或模块对整个系统影响及其有效性,就要进行消融实验。比如给定两个模块A和B,发现结合A和B之后效果提升了不少。但这并不能保证A和B分别是有意义的。所以需要进行分别验证:在基准上测试A,在基准上测试B,在基准上测试A+B,如果A+B的效果大于前两者,则表明提出的方法是有效的,反之则无效。

        综上就是验证为了提高某模型效率所想的几个思路而做的控制变量实验。

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  1. 周虹谷

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