Constraint-based learning reduces the burden of collecting labels by having
users specify general properties of structured outputs, such as constraints
imposed by physical laws. We propose a novel framework for simultaneously
learning these constraints and using them for supervision, bypassing the
difficulty of using domain expertise to manually specify constraints. Learning
requires a black-box simulator of structured outputs, which generates valid
labels, but need not model their corresponding inputs or the input-label
relationship. At training time, we constrain the model to produce outputs that
cannot be distinguished from simulated labels by adversarial training.
Providing our framework with a small number of labeled inputs gives rise to a
new semi-supervised structured prediction model; we evaluate this model on
multiple tasks --- tracking, pose estimation and time series prediction --- and
find that it achieves high accuracy with only a small number of labeled inputs.
In some cases, no labels are required at all.

本研究提出了一种基于约束学习的半监督学习方法，利用黑盒模拟器生成未标记数据进行训练并通过对抗训练实现生成模型的训练，提高模型在多种任务上的表现。