ProPaLL: Probabilistic Partial Label Learning

Deep neural networks achieve excellent performance in many real applications. However, they require a large-scale (CLL) (Ishida et al., 2017), the number of candidate labels training set with correctly labeled samples. Obtaining such a is only one less than the number of classes, making this task set is time-consuming, expensive, and, in some domains, impossible even more challenging.. due to discrepancies between labeling experts (Armato III et al., 2011). In consequence, many contemporary The standard approaches to partial and complementary label datasets are weakly labeled, forcing researchers to propose learning are incompatible with high-efficient stochastic optimization adequate learning strategies within the Weakly Supervised and cannot handle large-scale datasets (Liu & Dietterich, Learning paradigm (Zhou, 2018).