What ensemble methods are used in the state-of-the-art models?
When I surveyed the state-of-the-art methods of classification and detection, e.g. ImageNet, COCO, etc., I noticed that are few or even no references to the use of ensemble methods like bagging or boosting.
Is it a bad idea to use them?
However, I observed that many use ensemble in Kaggle competitions.
What makes it so different between the two groups of researchers?
In my opinion, it is not because ensemble methods are not good, just the state-of-the-art and Kaggle competitions are two different fields.
Kaggle competitions can be understood as an industry project where the target (accuracy, distance value, etc) is the most important, and they can select some computationally expensive way such as ensemble methods to reach it.
The state-of-the-art models in other ways belong to the research area, where the most important is the contribution for science, you can not just combine a lot of models then call it is the research (and so unfair with some small researcher groups). If you want to contribute something depend on ensemble idea, it should be like this paper.
