Ablation Study
Ablation study describe a procedure where certain parts of a neural network are removed, in order to gain a better understanding of the network’s behaviour.
According to Francois Chollet (primary author of the Keras framework), in a tweet in 2018 he said:
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.