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If all you features are binary, then, you don't need to apply normalization on them. Since their values are on the same scale already.


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Considering that you are making a minmax scaling, the only time in which there would be no risk of data leakage is if the minimum value on your training set equals the minimum value of the test set, and your maximum value on the training set equals the maximum value on the test set. In that circumstance the result of your scaling would be exactly the same as ...


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You can denoise a certain fraction of images (preferably 0.25-0.3) randomly for each epoch. Adding Gaussian noise to images gives better results.Refer this : Link


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