Recognition problems in long-tailed data, where the sample size per class is
heavily skewed, have recently gained importance because the distribution of the
sample size per class in a dataset is generally exponential unless the sample
size is intentionally adjusted. Various approaches have been devised to address
these problems. Recently, weight balancing, which combines well-known classical
regularization techniques with two-stage training, has been proposed. Despite
its simplicity, it is known for its high performance against existing methods
devised in various ways. However, there is a lack of understanding as to why
this approach is effective for long-tailed data. In this study, we analyze the
method focusing on neural collapse and cone effect at each training stage and
find that it can be decomposed into the increase in Fisher's discriminant ratio
of the feature extractor caused by weight decay and cross entropy loss and
implicit logit adjustment caused by weight decay and class-balanced loss. Our
analysis shows that the training method can be further simplified by reducing
the number of training stages to one while increasing accuracy.

研究了一种针对长尾数据的方法，基于权重平衡和两阶段训练结合的经典正则化技术，通过分析神经崩溃和锥效应，发现该方法是通过权重衰减和交叉熵损失以及类平衡损失调整的隐式 logit 方法增加特征提取器的 Fisher 判别比率。研究表明，通过将训练阶段的数量减少到一个并提高准确性，可以进一步简化训练方法。