  Partial Label Learning (PLL) tackles model learning from the data with
inexact labels under the assumption that training and test objects are in the
same distribution, i.e., closed-set scenario. Nevertheless, this assumption
does not hold in real-world open-set scenarios where test data may come from
Out-Of-Distribution (OOD), resulting in object detection failure and hence
significantly compromising the PLL model's security and trustworthiness. This
is a previously unexplored problem called Out-Of-Distribution Partial Label
Learning (OODPLL) that our newly proposed PLOOD framework can effectively
resolve. During the training phase, our framework leverages self-supervised
learning strategy to generate positive and negative samples for each object,
emulating in and out-of-distributions respectively. Under these distributions,
PLL methods can learn discriminative features for OOD objects. In the inference
phase, a novel Partial Energy (PE) scoring technique is proposed which
leverages the label confidence established during the above training phase to
mine the actual labels. In this way, the issue of inexact labeling in PLL can
be effectively addressed for significantly better performance in OOD object
detection. PLOOD is compared with SOTA PLL models and OOD scores on CIFAR-10
and CIFAR-100 datasets against various OOD datasets. The results demonstrate
the effectiveness of our PLOOD framework, significantly outperforming SOTA PLL
models and marking a substantial advancement in addressing PLL problems in
real-world OOD scenarios.
