Dense Contrastive Learning
Dense Contrastive Learning
**Dense Contrastive Learning** is a self-supervised learning method for dense prediction tasks. It implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images. Contrasting with regular contrastive loss, the contrastive loss is computed between the single feature vectors outputted by the global projection head, at the level of global feature, while the dense contrastive loss is computed between the dense feature vectors outputted by the dense projection head, at the level of local feature.
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
https://paperswithcode.com/paper/dense-contrastive-learning-for-self
Dense Contrastive Learning for Self-Supervised Visual Pre-Training