This is really interesting, but points out a key importance in training neural nets, which is to design your dataset and training to maximize generalization. For example, in the case of training a neural network for something that is highly safety critical, like an autonomous vehicle, it's important for vehicle and pedestrian detection to be as generalized as possible. In order to achieve high confidence in all sorts of lighting, weather, and angle conditions, the training data is augmented, which means it is manipulated randomly with color, saturation, contrast, blurring, shifting, mirroring, adding noise, etc. So adding random pixels, to potentially protect against dead pixels is also a great idea.