I don't think so - it is fairly common in simulations and statistical analysis as a way to explore the robustness of your results given small changes within th

I don't think so - it is fairly common in simulations and statistical analysis as a way to explore the robustness of your results given small changes within the tolerances you expect for your model. Usually it is applied to the input data, but training neural networks can be expensive so tinkering with the weights is much cheaper (which is the input to some gradient descent algorithm looking for a local minimum and gives insight into the stability of your local minimum).