1.In experiments, the PRODEN method also uses mixup and consistency training techniques for fair comparisons. What about other competitive baselines? I'd like to see how much the strong CC method could benefit from the representation training technique.
2.It is not clear why the proposed sample selection mechanism helps preserve the label distribution.
3.In App. B.2, a relaxed solution of Sinkhorn-Knopp algorithm is proposed. Why the relaxed problem guarantees to converge?Does Solar always run this relaxed version of Sinkhorn-Knopp?
4.How is gamma in the Sinknhorn-Knopp affect the performance?
5.How does the class distribution estimate for PRODEN in Figure 1?
Societal Impacts: The main negative impact is lower annotation costs may decrease the requirement for annotator employment.
Limitations: The experiments need to be further improved.
2.It is not clear why the proposed sample selection mechanism helps preserve the label distribution.