A Boo(n) for Evaluating Architecture Performance
This addresses a methodological issue for researchers in deep learning, but it is incremental as it refines existing evaluation practices.
The paper tackles the problem of comparing deep learning architectures by highlighting flaws in using best single model performance due to training stochasticity, and proposes a normalized expected best-out-of-n performance (Boo_n) as a correction method.
We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time a model is trained, one gets a different result due to random factors in the training process, which include random parameter initialization and random data shuffling. Reporting the best single model performance does not appropriately address this stochasticity. We propose a normalized expected best-out-of-$n$ performance ($\text{Boo}_n$) as a way to correct these problems.