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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.
Sounds awfully like Complexity to me. Maybe Deep-learning and AI would be better considered a social science instead?
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