  Contrastive representation learning has been outstandingly successful in
practice. In this work, we identify two key properties related to the
contrastive loss: (1) alignment (closeness) of features from positive pairs,
and (2) uniformity of the induced distribution of the (normalized) features on
the hypersphere. We prove that, asymptotically, the contrastive loss optimizes
these properties, and analyze their positive effects on downstream tasks.
Empirically, we introduce an optimizable metric to quantify each property.
Extensive experiments on standard vision and language datasets confirm the
strong agreement between both metrics and downstream task performance.
Remarkably, directly optimizing for these two metrics leads to representations
with comparable or better performance at downstream tasks than contrastive
learning.
  Project Page: https://tongzhouwang.info/hypersphere
  Code: https://github.com/SsnL/align_uniform ,
https://github.com/SsnL/moco_align_uniform
