Title: Two-Branch Label Distribution Learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: label distribution learning (LDL), Multi-label learning, label correlation, learning with ambiguity

Abstract: Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance, capturing comprehensive message by predicting description degree. However, existing approaches have primarily focused on single label distribution settings, limiting their applicability to tasks with only one output branch and rendering them sensitive to numerical fluctuations. For the first time, this paper introduces the problem of Two-Branch Label Distribution Learning (TBLDL), where an LDL model is supervised by two distinct label distributions. We assume that there is a correlation between two label branches,and by modeling this interdependence, we can effectively fuse cross-branch information to enhance predictive performance. Furthermore, for conventional LDL tasks involving only a single label distribution, our framework enables the generation of an auxiliary smooth distribution, thereby improving model robustness and accuracy. Finally, we apply TBLDL into several traditional LDL methods, demonstrating its broad compatibility and generalizability. Experimental results confirm that TBLDL not only handles both one and two-branch LDL tasks effectively but also consistently outperforms traditional LDL approaches.