  Semi-Supervised Learning (SSL) has been an effective way to leverage abundant
unlabeled data with extremely scarce labeled data. However, most SSL methods
are commonly based on instance-wise consistency between different data
transformations. Therefore, the label guidance on labeled data is hard to be
propagated to unlabeled data. Consequently, the learning process on labeled
data is much faster than on unlabeled data which is likely to fall into a local
minima that does not favor unlabeled data, leading to sub-optimal
generalization performance. In this paper, we propose FlatMatch which minimizes
a cross-sharpness measure to ensure consistent learning performance between the
two datasets. Specifically, we increase the empirical risk on labeled data to
obtain a worst-case model which is a failure case that needs to be enhanced.
Then, by leveraging the richness of unlabeled data, we penalize the prediction
difference (i.e., cross-sharpness) between the worst-case model and the
original model so that the learning direction is beneficial to generalization
on unlabeled data. Therefore, we can calibrate the learning process without
being limited to insufficient label information. As a result, the mismatched
learning performance can be mitigated, further enabling the effective
exploitation of unlabeled data and improving SSL performance. Through
comprehensive validation, we show FlatMatch achieves state-of-the-art results
in many SSL settings.
