your training wise your labels WT is your rammed up wait for your hands provides function f theta faxes your neural net that's the classification task and it could have some drop are in that world to be stochastic G of X is your data argumentation is also stochastic and you basically perform two different augmentations of your mini-batch get two different output CI and Zi to D and you make sure that Zi and Zi absolutely are close to each other using a square error loss or some some distance metric and you also make