We introduce MarginMatch, a new SSL approach combining consistency
regularization and pseudo-labeling, with its main novelty arising from the use
of unlabeled data training dynamics to measure pseudo-label quality. Instead of
using only the model's confidence on an unlabeled example at an arbitrary
iteration to decide if the example should be masked or not, MarginMatch also
analyzes the behavior of the model on the pseudo-labeled examples as the
training progresses, to ensure low quality predictions are masked out.
MarginMatch brings substantial improvements on four vision benchmarks in low
data regimes and on two large-scale datasets, emphasizing the importance of
enforcing high-quality pseudo-labels. Notably, we obtain an improvement in
error rate over the state-of-the-art of 3.25% on CIFAR-100 with only 25 labels
per class and of 3.78% on STL-10 using as few as 4 labels per class. We make
our code available at this https URL

MarginMatch 是一种结合一致性正则化和伪标记的新的 SSL 方法，其主要创新在于使用无标签数据的训练动态来衡量伪标签的质量，以确保屏蔽低质量预测；在低数据情况下，MarginMatch 在四个视觉基准测试以及两个大规模数据集上都取得了显著的改进，并且在每类只有 25 个标签的 CIFAR-100 上提高了 3.25％的错误率，在每类只有 4 个标签的 STL-10 上提高了 3.78％的错误率。

MarginMatch: 利用伪边际改进半监督学习

MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins

We developed a novel SSL approach to capture global consistency and
pixel-level local consistencies between differently augmented views of the same
images to accommodate downstream discriminative and dense predictive tasks. We
adopted the teacher-student architecture used in previous contrastive SSL
methods. In our method, the global consistency is enforced by aggregating the
compressed representations of augmented views of the same image. The
pixel-level consistency is enforced by pursuing similar representations for the
same pixel in differently augmented views. Importantly, we introduced an
uncertainty-aware context stabilizer to adaptively preserve the context gap
created by the two views from different augmentations. Moreover, we used Monte
Carlo dropout in the stabilizer to measure uncertainty and adaptively balance
the discrepancy between the representations of the same pixels in different
views.

本文提出了一种基于 SSL（自监督学习）的方法，通过全局一致性和像素级局部一致性来捕捉不同增强视图之间的一致性，引入了基于不确定性的上下文稳定器，使用 MC dropout 来衡量不确定性，并自适应平衡不同视图中相同像素的表征之间的差异。