title: Number of backprops as performance metric for neural networks
body: <p>I have been reading article about <a href="http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html" rel="nofollow noreferrer">SRCNN</a> and found that they are using "number of backprops" for evaluating how well network is performing, i.e. what network is able to learn after x backprops (as I understand). I would like to know what number of backprops actually means. Is this just the number of training data samples that there used during the training? Or maybe the number of mini-batches? Maybe it is one of the previous numbers multiplied by number of learnable parameters in the network? Or something completely different? Maybe there is some other more common name for this that I could loop up somewhere and read more about it because I was not able to find anything useful by searching "number of backprops" or "number of backpropagations"?</p>
  
  <p>Bonus question: how widely this metric is used and how good is it?</p>