The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g. by loss re-weighting, data re-sampling, or transfer learning from head-to tail-classes, but most of them adhere to the scheme of jointly learning representations and classifiers. In this work, we decouple the learning procedure into representation learning and classification, and systematically explore how different balancing strategies affect them for long-tailed recognition. The findings are surprising: (1) data imbalance might not be an issue in learning high-quality representations; (2) with representations learned with the simplest instance-balanced (natural) sampling, it is also possible to achieve strong long-tailed recognition ability at little cost by adjusting only the classifier. We conduct extensive experiments and set new state-of-the-art performance on common long-tailed benchmarks like ImageNet-LT, Places-LT and iNaturalist, showing that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification.
The paper tries to handle the class imbalance problem by decoupling the learning process into representation learning and classification, in contrast to the current methods that jointly learn both of them. They comprehensively study several sampling methods for representation learning and different strategies for classification. They find that instance-balanced sampling gives the best representation, and simply adjusting the classifier will equip the model with long-tailed recognition ability. They achieve start of art on long-tailed data (ImageNet-LT, Places-LT and iNaturalist).
In general, this is paper is an interesting paper. The author propose that instance-balanced sampling already learns the best and most generalizable representations, which is out of common expectation. They perform extensive experiment to illustrate their points.
--Writing:
This paper is well written in English and is well structured. And there are two typos. One is in the second row of page 3, "… a more continuous decrease [in in] class labels …" and the other one is in the first paragraph of section 5.4, "… report state-of-art results [on on] three common long-tailed benchmarks …". 
--Introduction and review:
The authors do a comprehensive literature review, listing the main directions for solving the long-tailed recognition problem. They emphasis that these methods all jointly learn the representations and classifiers, which "make it unclear how the long-tailed recognition ability is achieved-is it from learning a better representation or by handling the data imbalance better via shifting classifier decision boundaries". This motivate them to decouple representation learning and classification.
--Experiment:
Since this paper decouples the representation learning and classification to "make it clear" whether the long-tailed recognition ability is achieved from better representation or more balanced classifier, I recommend that authors show us some visualization of the feature map besides number on performance. Because I am confused and difficult to image what "better representation" actually looks like. 
The authors conduct experiment with ResNeXt-{10,50,101,151}, and mainly use ResNeXt-50 for analysis. Will other networks get similar results as that of ResNeXt-50 shown in Figure 1?
When showing the results, like Figure 1, 2 and Table 2, it would be better to mention the parameters chosen for \tau-normalization and other methods.
Conclusion:
I tend to accept this paper since it is interesting and renews our understanding of the long-tailed recognition ability of neural network and sampling strategies. What's more, he experiment is comprehensive and rigorous.