Making Partial-Label Datasets Easier: A Simple Yet Highly Effective Data Augmentation for Deep Partial-Label Learning

Partial-label learning (PLL), which refers to the classifica-tion task where each training instance is associated with a set of candidate-labels, among which only one is correct. While recent advances in deepPLL primarily focus on designing sophisticated disambiguation strate-gies, the role of PLL-specific data augmentation has been less explored.Most methods often treat data augmentation as a generic plug-in, over-looking its potential interaction with the label disambiguation processunique to PLL. To address this gap, we propose a local-ambiguity-awareaugmentation framework that consists of label identification and modelregularization modules and accommodates a wide range of PLL algo-rithms. Within this framework, a PLL-specific augmentation strategybased on a mixing-renormalization process is applied to partial-labeldata. Theoretically, we show that our augmentation can make the PLLdataset easier by reducing the local ambiguity degree, and it induces mul-tiple implicit regularizations during training thereby facilitating highlyeffective disambiguation. Extensive experiments on both benchmark andreal-world datasets demonstrate that our approach consistently improvesthe generalization performance of state-of-the-art PLL algorithms.