Self-supervised visual representation learning aims to learn useful
representations without relying on human annotations. Joint embedding approach
bases on maximizing the agreement between embedding vectors from different
views of the same image. Various methods have been proposed to solve the
collapsing problem where all embedding vectors collapse to a trivial constant
solution. Among these methods, contrastive learning prevents collapse via
negative sample pairs. It has been shown that non-contrastive methods suffer
from a lesser collapse problem of a different nature: dimensional collapse,
whereby the embedding vectors end up spanning a lower-dimensional subspace
instead of the entire available embedding space. Here, we show that dimensional
collapse also happens in contrastive learning. In this paper, we shed light on
the dynamics at play in contrastive learning that leads to dimensional
collapse. Inspired by our theory, we propose a novel contrastive learning
method, called DirectCLR, which directly optimizes the representation space
without relying on an explicit trainable projector. Experiments show that
DirectCLR outperforms SimCLR with a trainable linear projector on ImageNet.

本文介绍了一种自监督学习方法 —— 对比学习。为了避免嵌入向量全部收敛到常数解的问题，这篇文章提出了一种新的对比学习方法 DirectCLR，该方法直接优化表示空间而不依赖于显式可训练的投影仪。实验表明，DirectCLR 在 ImageNet 上的表现优于 SimCLR。