Ablation Study 消融实验

The term “ablation study” is often use …


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。


         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.



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  1. 周虹谷