Weakness:
A semi-supervised feature learning baseline is missing.
This is my main concern about the paper. The key argument in the paper is that feature learning and classifier learning should 1) be decoupled, 2) use random sampling and class-balanced sampling respectively, 3) train on all labels and only ground-truth labels respectively. The authors, therefore, propose a carefully designed alternate sampling strategy.
However, a more straightforward strategy could be 1) train a feature extractor ( f
in the paper) and a classifier ( g ′
in the paper) using random sampling and any semi-supervised learning method on all data, then 2) freeze the feature extractor ( f
) and train a new classifier ( g
in the paper) using class-balanced sampling on data with ground-truth labels. Compared with the alternate sampling strategy proposed in this paper, the semi-supervised feature learning baseline will take less implementation effort and is easier to combine with any semi-supervised learning methods.
The baseline seems to be missing in the paper. Although the naive baseline may not give the best performance, it should be compared to justify the sophisticatedly designed alternate sampling strategy.
References are not up-to-date.
All references in the paper are in or before 2020. In fact, much research progress has been made since then. For example, some recent works [1, 2] study the class-imbalanced semi-supervised learning as well, a discussion on these methods should be necessary. A recent survey on long-tailed learning [3] could be a useful resource to help update the related works in the paper.
Minor issues: "The model is the fine-tuned on the combination of ..." -> "The model is then fine-tuned on the combination of ..."
[1] Su et al., A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification, CVPR 2021
[2] Wei et al., CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning, CVPR 2021
[3] Deep Long-Tailed Learning: A Survey, arXiv 2021
1) train a feature extractor ( f in the paper) and a classifier ( g ′ in the paper) using random sampling and any semi-supervised learning method on all data, then