Decoupled Imbalanced Label Distribution Learning

Abstract

Label Distribution Learning (LDL) has been successfully implemented in numerous practical applications. However, the imbalance in label distributions presents a significant challenge due to the substantial variation in annotation information. To tackle this issue, we introduce Decoupled Imbalance Label Distribution Learning (DILDL), which decomposes the imbalanced label distribution into a dominant label distribution and a non-dominant label distribution. Our empirical findings reveal that an excessively high description degree of dominant labels can result in substantial gradient information attenuation for non-dominant labels during the learning process. Therefore, we employ the decoupling approach to balance the description degrees of both dominant and non-dominant labels independently. Furthermore, we align the feature representations with the representations of dominant and non-dominant labels separately, aiming to effectively mitigate the distribution shift problem. Experimental results demonstrate that our proposed DILDL outperforms other state-of-the-art methods for imbalance label distribution learning.