  Confidence-based pseudo-labeling is among the dominant approaches in
semi-supervised learning (SSL). It relies on including high-confidence
predictions made on unlabeled data as additional targets to train the model. We
propose ProtoCon, a novel SSL method aimed at the less-explored label-scarce
SSL where such methods usually underperform. ProtoCon refines the pseudo-labels
by leveraging their nearest neighbours' information. The neighbours are
identified as the training proceeds using an online clustering approach
operating in an embedding space trained via a prototypical loss to encourage
well-formed clusters. The online nature of ProtoCon allows it to utilise the
label history of the entire dataset in one training cycle to refine labels in
the following cycle without the need to store image embeddings. Hence, it can
seamlessly scale to larger datasets at a low cost. Finally, ProtoCon addresses
the poor training signal in the initial phase of training (due to fewer
confident predictions) by introducing an auxiliary self-supervised loss. It
delivers significant gains and faster convergence over state-of-the-art across
5 datasets, including CIFARs, ImageNet and DomainNet.
