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Hi thank you for your exciting work. I've noticed a potential problem regarding to the evaluation procedure. To best of my knowledge currently the best model is selected based on the test data. However this is not desirable since in real conditions it is not possible to chose the model based on the testing data. One probable issue rather than getting comparable performances is possibility for overfitting. Altough test data is not used for gradient updates, model is chosen based on the best performing test data. Therefore, we have no way of knowing if the proposed model is just better at leaking the information via model selection. One extreme case is if you randomly guess enough times on test set you can get 100%. That's generally why the validation split is used in prior works 1.
The text was updated successfully, but these errors were encountered:
Hi thank you for your exciting work. I've noticed a potential problem regarding to the evaluation procedure. To best of my knowledge currently the best model is selected based on the test data. However this is not desirable since in real conditions it is not possible to chose the model based on the testing data. One probable issue rather than getting comparable performances is possibility for overfitting. Altough test data is not used for gradient updates, model is chosen based on the best performing test data. Therefore, we have no way of knowing if the proposed model is just better at leaking the information via model selection. One extreme case is if you randomly guess enough times on test set you can get 100%. That's generally why the validation split is used in prior works 1.
The text was updated successfully, but these errors were encountered: