  Partial label learning (PLL) is a typical weakly supervised learning
framework, where each training instance is associated with a candidate label
set, among which only one label is valid. To solve PLL problems, typically
methods try to perform disambiguation for candidate sets by either using prior
knowledge, such as structure information of training data, or refining model
outputs in a self-training manner. Unfortunately, these methods often fail to
obtain a favorable performance due to the lack of prior information or
unreliable predictions in the early stage of model training. In this paper, we
propose a novel framework for partial label learning with meta objective guided
disambiguation (MoGD), which aims to recover the ground-truth label from
candidate labels set by solving a meta objective on a small validation set.
Specifically, to alleviate the negative impact of false positive labels, we
re-weight each candidate label based on the meta loss on the validation set.
Then, the classifier is trained by minimizing the weighted cross entropy loss.
The proposed method can be easily implemented by using various deep networks
with the ordinary SGD optimizer. Theoretically, we prove the convergence
property of meta objective and derive the estimation error bounds of the
proposed method. Extensive experiments on various benchmark datasets and
real-world PLL datasets demonstrate that the proposed method can achieve
competent performance when compared with the state-of-the-art methods.
