  Recognition problems in long-tailed data, in which the sample size per class
is heavily skewed, have 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 methods 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 compared with existing
methods devised in various ways. However, there is a lack of understanding as
to why this method is effective for long-tailed data. In this study, we analyze
weight balancing by focusing on neural collapse and the cone effect at each
training stage and found that it can be decomposed into an 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 enables the training method to be further
simplified by reducing the number of training stages to one while increasing
accuracy. Code is available at
https://github.com/HN410/Exploring-Weight-Balancing-on-Long-Tailed-Recognition-Problem.
