Rethinking Classifier Re-Training in Long-Tailed Recognition: Label Over-Smooth Can Balance
Siyu Sun Han Lu Jiangtong Li Yichen Xie Tianjiao Li Xiaokang Yang Liqing Zhang Junchi Yan
In the field of long-tailed recognition, the Decoupled Training paradigm has shown exceptional promise by dividing training into two stages: representation learning and classifier re-training.  While previous work has tried to improve both stages simultaneously, this complicates isolating the effect of classifier re-training.  Recent studies reveal that simple regularization can produce strong feature representations, highlighting the need to reassess classifier re-training methods.  In this study, we revisit classifier re-training methods based on a unified feature representation and re-evaluate their performances. 
We propose two new metrics, Logits Magnitude and Regularized Standard Deviation, to compare the differences and similarities between various methods. 
Using these two newly proposed metrics, we demonstrate that when the Logits Magnitude across classes is nearly balanced, further reducing its overall value can effectively decrease errors and disturbances during training, leading to better model performance. 
Based on our analysis using these metrics, we observe that adjusting the logits could improve model performance, leading us to develop a simple label over-smoothing approach to adjust the logits without requiring prior knowledge of class distribution.
This method softens the original one-hot labels by assigning a probability slightly higher than $\frac{1}{K}$ to the true class and slightly lower than $\frac{1}{K}$ to the other classes, where $K$ is the number of classes.
Our method achieves state-of-the-art performance on various imbalanced datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist2018.
Long-Tailed Recognition and Decoupled Training
In this study, we propose a new logits-based metric for classifier re-training in long-tailed recognition, introducing a simple retargeting method "Label Over-Smooth" that achieves state-of-the-art performance on imbalanced datasets.