Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training

Understand

Self-training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training.

• In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the classifier.
• This formulation facilitates a practical annealing strategy for label assignment and allows for the inclusion of prior knowledge on class proportions via flexible upper bound constraints.
• The solutions to these assignment problems can be efficiently approximated using Sinkhorn iteration, thus enabling their use in the inner loop of standard stochastic optimization algorithms.