Patch-level Representation Learning for Self-supervised Vision Transformers
Patch-level Representation Learning for Self-supervised Vision Transformers
Recent self-supervised learning (SSL) methods have shown impressive results in learning visual representations from unlabeled images. This paper aims to improve their performance further by utilizing the architectural advan-tages of the underlying neural network, as the current state-of-the-art visual pretext tasks for SSL do not enjoy the ben-efit, i.e., they …