I agree that is what the post describes and that it could be a useful process. I don't think "overfitting" describes that process though. Overfitting describes increasing your performance on the training set to the extent that your model performs worse on the data it is used on.
If overfitting is happening here then it wouldn't be beneficial. There is no reason to prefer that your model be better on the training set if you are going to use it to collect batman images across a film. It would be better if your model wasn't overfit, if it performed better on your dataset, then it would collect more images.
If overfitting is happening here then it wouldn't be beneficial. There is no reason to prefer that your model be better on the training set if you are going to use it to collect batman images across a film. It would be better if your model wasn't overfit, if it performed better on your dataset, then it would collect more images.