Label Distribution Learning

Label distribution learning (LDL) addresses the challenge of assigning probabilities to multiple labels for a single data point, moving beyond traditional single-label classification. Current research focuses on improving LDL's robustness to noisy or incomplete label distributions, often employing graph neural networks, adaptive fusion networks, or novel loss functions like Kullback-Leibler divergence to capture label correlations and handle uncertainty. This field is significant because it allows for more nuanced modeling of real-world phenomena where labels are inherently ambiguous or multi-faceted, impacting applications such as facial expression recognition, age estimation, and disease severity prediction.