> That means that a model with the highest accuracy on a noisy dataset will likely be the best model on the clean dataset.
That's exactly the opposite of what this article says.
That's exactly the opposite of what this article says.
The observation that a less faulty model is likely less accurate on a noisy validation set than a more faulty model, doesn't change the fact that there must be faulty models with higher accuracy than a perfect model on a noisy validation set.