- If I understand correctly, the method requires maintaining a probability vector for each data point. This is not an issue for small data sets with few classes, but can become a problem at ImageNet scale. I did not find any comment regarding this issue in the main paper or in the supplement. Could the authors please elaborate on this? - From Table 2 b) it seems that for 40% label noise on CIFAR10 the method is reasonably robust to the hyper parameter values. Does this observation transfer to other corruption percentages and data sets? - Additional experiments on larger data sets would be nice (but I understand that compute might be an issue). --- Thanks for the author response. I still think maintaining the probabilities might become an issue, in particular at large batch size, but I don't think this aspect is critical. Generally, the response addressed my concerns well.
- Additional experiments on larger data sets would be nice (but I understand that compute might be an issue). --- Thanks for the author response. I still think maintaining the probabilities might become an issue, in particular at large batch size, but I don't think this aspect is critical. Generally, the response addressed my concerns well.