  Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and
unlabeled data to enhance model performance. Conventional SSL methods operate
under the assumption that labeled and unlabeled data share the same label
space. However, in practical real-world scenarios, especially when the labeled
training dataset is limited in size, some classes may be totally absent from
the labeled set. To address this broader context, we propose a general approach
to augment existing SSL methods, enabling them to effectively handle situations
where certain classes are missing. This is achieved by introducing an
additional term into their objective function, which penalizes the
KL-divergence between the probability vectors of the true class frequencies and
the inferred class frequencies. Our experimental results reveal significant
improvements in accuracy when compared to state-of-the-art SSL, open-set SSL,
and open-world SSL methods. We conducted these experiments on two benchmark
image classification datasets, CIFAR-100 and STL-10, with the most remarkable
improvements observed when the labeled data is severely limited, with only a
few labeled examples per class
