  Semi-supervised learning holds great promise for many real-world
applications, due to its ability to leverage both unlabeled and expensive
labeled data. However, most semi-supervised learning algorithms still heavily
rely on the limited labeled data to infer and utilize the hidden information
from unlabeled data. We note that any semi-supervised learning task under the
self-training paradigm also hides an auxiliary task of discriminating label
observability. Jointly solving these two tasks allows full utilization of
information from both labeled and unlabeled data, thus alleviating the problem
of over-reliance on labeled data. This naturally leads to a new generic and
efficient learning framework without the reliance on any domain-specific
information, which we call FlexSSL. The key idea of FlexSSL is to construct a
semi-cooperative "game", which forges cooperation between a main
self-interested semi-supervised learning task and a companion task that infers
label observability to facilitate main task training. We show with theoretical
derivation of its connection to loss re-weighting on noisy labels. Through
evaluations on a diverse range of tasks, we demonstrate that FlexSSL can
consistently enhance the performance of semi-supervised learning algorithms.
