  Multi-label image classification datasets are often partially labeled where
many labels are missing, posing a significant challenge to training accurate
deep classifiers. However, the powerful Mixup sample-mixing data augmentation
cannot be well utilized to address this challenge, as it cannot perform linear
interpolation on the unknown labels to construct augmented samples. In this
paper, we propose LogicMix, a Mixup variant designed for such partially labeled
datasets. LogicMix mixes the sample labels by logical OR so that the unknown
labels can be correctly mixed by utilizing OR's logical equivalences, including
the domination and identity laws. Unlike Mixup, which mixes exactly two
samples, LogicMix can mix multiple ($\geq2$) partially labeled samples,
constructing visually more confused augmented samples to regularize training.
LogicMix is more general and effective than other compared Mixup variants in
the experiments on various partially labeled dataset scenarios. Moreover, it is
plug-and-play and only requires minimal computation, hence it can be easily
inserted into existing frameworks to collaborate with other methods to improve
model performance with a negligible impact on training time, as demonstrated
through extensive experiments. In particular, through the collaboration of
LogicMix, RandAugment, Curriculum Labeling, and Category-wise Fine-Tuning, we
attain state-of-the-art performance on MS-COCO, VG-200, and Pascal VOC 2007
benchmarking datasets. The remarkable generality, effectiveness, collaboration,
and simplicity suggest that LogicMix promises to be a popular and vital data
augmentation method.
