- More evaluation would have been welcome, especially on CIFAR-10 in the full label and lower label scenarios.
- The CIFAR-10 results are a little disappointing with respect to temporal ensembles (although the results are comparable and the proposed approach has other advantages)
- An evaluation on the more challenging STL-10 dataset would have been welcome. Comments
- The SVNH evaluation suggests that the model is better than pi an temporal ensembling especially in the low-label scenario. With this in mind, it would have been nice to see if you can confirm this on CIFAR-10 too (i.e. show results on CIFAR-10 with less labels)
- I would would have like to have seen what the CIFAR-10 performance looks like with all labels included.
- It would be good to include in the left graph in fig 3 the learning curve for a model without any mean teacher or pi regularization for comparison, to see if mean teacher accelerates learning or slows it down.
- I'd be interested to see if the exponential moving average of the weights provides any benefit on it's own, without the additional consistency cost.
- It would be good to include in the left graph in fig 3 the learning curve for a model without any mean teacher or pi regularization for comparison, to see if mean teacher accelerates learning or slows it down.