Unsupervised representation learning aims at describing raw data efficiently to solve various downstream tasks. It has been approached with many techniques, such as manifold learning, diffusion maps, or more recently self-supervised learning. Those techniques are arguably all based on the underlying assumption that target functions, associated with future downstream tasks, have low variations in densely populated regions of the input space. Unveiling minimal variations as a guiding principle behind unsupervised representation learning paves the way to better practical guidelines for self-supervised learning algorithms.

翻译：未经监督的代表性学习旨在有效地描述原始数据,以便解决各种下游任务,已经与许多技术进行了接触,例如多重学习、传播地图或最近自我监督的学习,这些技术可以说都基于以下基本假设:目标功能,与未来的下游任务相关联,在输入空间人口稠密的地区差异较小。在未经监督的代表权学习背后,将最小的差异作为指导原则,为改进自我监督的学习算法的实用指南铺平了道路。