Neural Networks (NN) have been built to solve universal function approximation problems. Some architectures as Convolutional Neural Networks (CNN) are dedicated to classification in the context of image distortion. They have naturally been considered in the community to perform side-channel attacks showing reasonably good results on trace sets exposing time misalignment. However, even in settings where these timing distortions are not present, NN have produced better results than legacy attacks. Recently in TCHES 2020, auto-encoders have been used as preprocessing for noise reduction. The main idea is to train an auto-encoder using as inputs noisy traces and less noisy traces so that the auto-encoder is able to remove part of the noise in the attack dataset. We propose to extend this idea of using NN for pre-processing by not only considering the noise-reduction but to translate data between two side-channel domains. In a nutshell, clean (or less noisy) traces may not be available to an attacker, but similar traces that are easier to attack may be obtainable. Availability of such traces can be leveraged to learn how to translate difficult traces to easy ones to increase attackability.