As the class size grows, maintaining a balanced dataset across many classes is challenging because the data are long-tailed in nature; it is even impossible when the sample-of-interest co-exists with each other in one collectable unit, e.g., multiple visual instances in one image. Therefore, long-tailed classification is the key to deep learning at scale. However, existing methods are mainly based on re-weighting/re-sampling heuristics that lack a fundamental theory. In this paper, we establish a causal inference framework, which not only unravels the whys of previous methods, but also derives a new principled solution. Specifically, our theory shows that the SGD momentum is essentially a confounder in long-tailed classification. On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head.

Weaknesses: Despite its intriguing new perspective, the paper has some weaknesses that need to be further addressed. First, the paper lacks experimental comparisons to many other long-tail classification methods such as LDAM, balanced loss, BNN, even though they were mentioned in related work. Second, the use of multi-head strategy is not related to the claimed theoretical founding and it makes the judgement on the effectiveness of the theoretical framework more difficult. To the reviewer's point view, a fairer comparison would be just using K 1 just as other imbalanced classification framework. Third, the final form of the de-confounding training is very similar to previous works with the only difference being the hyperparameter gamma in equation 7. It is unclear to the reviewer whether the performance improvement comes from tuning the hyperparamter which is not directly inspired from the theoretical framework.

As the class size grows, maintaining a balanced dataset across many classes is challenging because the data are long-tailed in nature; it is even impossible when the sample-of-interest co-exists with each other in one collectable unit, e.g., multiple visual instances in one image. Therefore, long-tailed classification is the key to deep learning at scale. However, existing methods are mainly based on re-weighting/re-sampling heuristics that lack a fundamental theory. In this paper, we establish a causal inference framework, which not only unravels the whys of previous methods, but also derives a new principled solution. Specifically, our theory shows that the SGD momentum is essentially a confounder in long-tailed classification. On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head.