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.

本文提出了FlatMatch方法，通过最小化交叉尖锐度度量来保持两个数据集的一致学习性能，增加标记数据上的经验风险以获得一种最坏模型，然后利用未标记数据的丰富性惩罚最坏模型与原始模型之间的预测差异，从而使学习方向有利于未标记数据的泛化，进而校准学习过程，缓解不匹配的学习性能，进一步实现对未标记数据的有效利用和提高SSL性能。经过综合验证，我们展示了FlatMatch在许多SSL设置中实现了最先进的结果。

FlatMatch：利用交叉锐度连接标注数据和无标签数据进行半监督学习