高赞答案：
An ablation study typically refers to removing some “feature” of the model or algorithm, and seeing how that affects performance.
Examples:
- An LSTM has 4 gates: feature, input, output, forget. We might ask: are all 4 necessary? What if I remove one? Indeed, lots of experimentation has gone into LSTM variants, the GRU being a notable example (which is simpler).
- If certain tricks are used to get an algorithm to work, it’s useful to know whether the algorithm is robust to removing these tricks. For example, DeepMind’s original DQN paper reports using (1) only periodically updating the reference network and (2) using a replay buffer rather than updating online. It’s very useful for the research community to know that both these tricks are necessary, in order to build on top of these results.
- If an algorithm is a modification of a previous work, and has multiple differences, researchers want to know what the key difference is.
Simpler is better (inductive prior towards simpler model classes). If you can get the same performance with two models, prefer the simpler one.
知乎上简单的解释：
模型简化测试。
看看取消掉一些模块后性能有没有影响。
根据奥卡姆剃刀法则，简单和复杂的方法能达到一样的效果，那么简单的方法更可靠。
实际上ablation study就是为了研究模型中所提出的一些结构是否有效而设计的实验。
比如你提出了某某结构，但是要想确定这个结构是否有利于最终的效果，那就要将去掉该结构的网络与加上该结构的网络所得到的结果进行对比，这就是ablation study