There are many known ways to overcome overfitting or make a model generalize better to unseen data.
Here I would like to ask if normalizing/standardizing/similiraizing the train and test data is a plausible approach.
By similarizing I mean making the images look alike by using some function that could be a Neural Network itself. I know that normally one would approach this the opposite way by augmenting and therefore increasing the variation in the training data. But is also possible to improve the model by restricting the variation of the training and test data?
I know that this may not be the best approach and maybe too complicated but I see some use cases where known techniques of preventing overfitting aren't applicable. In those cases, having a network that can normalize/standardize/similarize the "style" of different images could be very useful.
Unfortunately I didn't find a single paper discussing this approach.