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Semi-Supervised image classification is one of the most fundamental problemin computer vision, which significantly reduces the need for human labor. Inthis paper, we introduce a new semi-supervised learning algorithm - SimMatchV2,which formulates various consistency regularizations between labeled andunlabeled data from the graph perspective. In SimMatchV2, we regard theaugmented view of a sample as a node, which consists of a label and itscorresponding representation. Different nodes are connected with the edges,which are measured by the similarity of the node representations. Inspired bythe message passing and node classification in graph theory, we propose fourtypes of consistencies, namely 1) node-node consistency, 2) node-edgeconsistency, 3) edge-edge consistency, and 4) edge-node consistency. We alsouncover that a simple feature normalization can reduce the gaps of the featurenorm between different augmented views, significantly improving the performanceof SimMatchV2. Our SimMatchV2 has been validated on multiple semi-supervisedlearning benchmarks. Notably, with ResNet-50 as our backbone and 300 epochs oftraining, SimMatchV2 achieves 71.9\% and 76.2\% Top-1 Accuracy with 1\% and10\% labeled examples on ImageNet, which significantly outperforms the previousmethods and achieves state-of-the-art performance. Code and pre-trained modelsare available at\href{https://github.com/mingkai-zheng/SimMatchV2}{https://github.com/mingkai-zheng/SimMatchV2}.

SimMatchV2: Semi-Supervised Learning with Graph Consistency