Projection Head

Projection heads are additional neural network layers appended to encoders in self-supervised learning, particularly contrastive learning methods like SimCLR, to improve the quality of learned representations. Current research focuses on understanding their function—including the effects of non-linearity, dimensionality, and sparsity—and optimizing their design, for example, through pretrained embeddings or ensemble methods to enhance model reliability and performance in downstream tasks. These investigations aim to improve the efficiency and robustness of self-supervised learning, impacting various applications such as image classification and federated learning by yielding better feature representations from unlabeled data.

Papers