Class-Balanced Regularization for Long-Tailed Recognition

Abstract

Long-tailed recognition performs poorly on minority classes. The extremely imbalanced distribution of classifier weight norms leads to a decision boundary biased toward majority classes. To address this issue, we propose Class-Balanced Regularization to balance the distribution of classifier weight norms so that the model can make more balanced and reasonable classification decisions. In detail, CBR separately adjusts the regularization factors based on L2 regularization to be correlated with the class sample frequency positively, rather than using a fixed regularization factor. CBR trains balanced classifiers by increasing the L2 norm penalty for majority classes and reducing the penalty for minority classes. Since CBR is mainly used for classification adjustment instead of feature extraction, we adopt a two-stage training algorithm. In the first stage, the network with the traditional empirical risk minimization is trained, and in the second stage, CBR for classifier adjustment is applied. To validate the effectiveness of CBR, we perform extensive experiments on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets. The results demonstrate that CBR significantly improves performance by effectively balancing the distribution of classifier weight norms.