  This work presents a new strategy for multi-class classification that
requires no class-specific labels, but instead leverages pairwise similarity
between examples, which is a weaker form of annotation. The proposed method,
meta classification learning, optimizes a binary classifier for pairwise
similarity prediction and through this process learns a multi-class classifier
as a submodule. We formulate this approach, present a probabilistic graphical
model for it, and derive a surprisingly simple loss function that can be used
to learn neural network-based models. We then demonstrate that this same
framework generalizes to the supervised, unsupervised cross-task, and
semi-supervised settings. Our method is evaluated against state of the art in
all three learning paradigms and shows a superior or comparable accuracy,
providing evidence that learning multi-class classification without multi-class
labels is a viable learning option.
