  Learning with few labeled data has been a longstanding problem in the
computer vision and machine learning research community. In this paper, we
introduced a new semi-supervised learning framework, SimMatch, which
simultaneously considers semantic similarity and instance similarity. In
SimMatch, the consistency regularization will be applied on both semantic-level
and instance-level. The different augmented views of the same instance are
encouraged to have the same class prediction and similar similarity
relationship respected to other instances. Next, we instantiated a labeled
memory buffer to fully leverage the ground truth labels on instance-level and
bridge the gaps between the semantic and instance similarities. Finally, we
proposed the \textit{unfolding} and \textit{aggregation} operation which allows
these two similarities be isomorphically transformed with each other. In this
way, the semantic and instance pseudo-labels can be mutually propagated to
generate more high-quality and reliable matching targets. Extensive
experimental results demonstrate that SimMatch improves the performance of
semi-supervised learning tasks across different benchmark datasets and
different settings. Notably, with 400 epochs of training, SimMatch achieves
67.2\%, and 74.4\% Top-1 Accuracy with 1\% and 10\% labeled examples on
ImageNet, which significantly outperforms the baseline methods and is better
than previous semi-supervised learning frameworks. Code and pre-trained models
are available at https://github.com/KyleZheng1997/simmatch.
