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Stating the obvious: a lot of current deep learning tricks are overfit to the validation sets of well-known benchmarks, including CIFAR10. It's nice to see this quantified. This has been a problem with ImageNet since at least 2015.https://arxiv.org/abs/1806.00451
If publishing your paper requires you to select specific ideas, architectures, and hyperparameters according to a fixed validation set, then it's not longer a validation set, it's a training set. And there are no guarantees that the selected ideas will generalize to real data.
Deep learning research, along so many axes, does not follow the scientific method. Validation set overfitting is a notable point. Also: use of weak benchmarks; impossibility to clearly link an empirical result to the big idea proposed by a paper (because of too many moving parts)
...issues with reproducibility of most papers; post-selection of results; lack of significance testing when comparing (often high-variance) empirical results...
If you're doing a Kaggle competition and you're evaluating your models / ideas according to a fixed validation split of the training data (plus the public leaderboard), you will consistently underperform on the private leaderboard. The same is true in research at large.
Here is a very simple recommendation to help you overcome this: use a higher-entropy validation process, such as k-fold validation, or even better, iterated k-fold validation with shuffling. Only check your results on the official validation set at the very end.
Yes, it's more expensive, but that cost itself is a regularization factor: it will force you to try fewer ideas instead of throwing spaghetti at the wall and seeing what sticks.
I wish managers here understood this :D
The "Guilty pleasure" of ML
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