 Note that each time a random example formula_58 is presented to the model, a new corrupted version is generated stochastically on the basis of formula_60.The above-mentioned training process could be developed with any kind of corruption process.Some examples might be "additive isotropic Gaussian noise, Masking noise" (a fraction of the input chosen at random for each example is forced to 0) or "Salt-and-pepper noise" (a fraction of the input chosen at random for each example is set to its minimum or maximum value with uniform probability).