Sensitivity to inconsistent data


Thanks for the great talk. How sensitive are the models to inconsistent labels? Say, we have 10% inconsistently labeled data in the training set. Would it cause proportional deterioration in the model, i.e., 10% drop in the accuracy, or would it cause super-linear deterioration, e.g., 50 % drop in the accuracy?

Thanks a lot!