You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: It is infeasible to compute the \operatorname{argmin} and \operatorname{argmax} across the wholetraining set. Additionally, it might lead to poor training, as mislabelled andpoorly imaged faces would dominate the hard positives and negatives.There are two obvious choices that avoid this issue:•Generate triplets offline every n steps, using the most recentnetwork checkpoint and computing the \operatorname{argmin} and \operatorname{argmax} on a subsetof the data.•Generate triplets online. This can be done by selecting the hardpositive/negative exemplars from within a mini-batch. To have a meaningful representation of the anchor-positive distances, it needsto be ensured that a minimal number of exemplars of any one identity is presentin each mini-batch. In our experiments we sample the training data such thataround 40 faces are selected per identity per mini-batch. Additionally, randomlysampled negative faces are added to each mini-batch.

Question: Could we consider "randomly sampled negative faces added to each mini-batch" as a mean of making the model more regularized against negative faces?
Model can be regularized against negative and poorly labeled faces by adding hard positive and negative examples in each mini-batch