Semi-Supervised image classification is one of the most fundamental problem
in computer vision, which significantly reduces the need for human labor. In
this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2,
which formulates various consistency regularizations between labeled and
unlabeled data from the graph perspective. In SimMatchV2, we regard the
augmented view of a sample as a node, which consists of a label and its
corresponding representation. Different nodes are connected with the edges,
which are measured by the similarity of the node representations. Inspired by
the message passing and node classification in graph theory, we propose four
types of consistencies, namely 1) node-node consistency, 2) node-edge
consistency, 3) edge-edge consistency, and 4) edge-node consistency. We also
uncover that a simple feature normalization can reduce the gaps of the feature
norm between different augmented views, significantly improving the performance
of SimMatchV2. Our SimMatchV2 has been validated on multiple semi-supervised
learning benchmarks. Notably, with ResNet-50 as our backbone and 300 epochs of
training, SimMatchV2 achieves 71.9\% and 76.2\% Top-1 Accuracy with 1\% and
10\% labeled examples on ImageNet, which significantly outperforms the previous
methods and achieves state-of-the-art performance. Code and pre-trained models
are available at
\href{https://github.com/mingkai-zheng/SimMatchV2}{this https URL}.

图像半监督分类问题中，引入了一种新的半监督学习算法 SimMatchV2，该算法从图的角度提出了不同类型的一致性正则化，并揭示了简单特征归一化可显著提高 SimMatchV2 的性能，在多个半监督学习基准测试上显著优于以前的方法并取得了最先进的性能。