  In this work, we propose Reciprocal Distribution Alignment (RDA) to address
semi-supervised learning (SSL), which is a hyperparameter-free framework that
is independent of confidence threshold and works with both the matched
(conventionally) and the mismatched class distributions. Distribution mismatch
is an often overlooked but more general SSL scenario where the labeled and the
unlabeled data do not fall into the identical class distribution. This may lead
to the model not exploiting the labeled data reliably and drastically degrade
the performance of SSL methods, which could not be rescued by the traditional
distribution alignment. In RDA, we enforce a reciprocal alignment on the
distributions of the predictions from two classifiers predicting pseudo-labels
and complementary labels on the unlabeled data. These two distributions,
carrying complementary information, could be utilized to regularize each other
without any prior of class distribution. Moreover, we theoretically show that
RDA maximizes the input-output mutual information. Our approach achieves
promising performance in SSL under a variety of scenarios of mismatched
distributions, as well as the conventional matched SSL setting. Our code is
available at: https://github.com/NJUyued/RDA4RobustSSL.
